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Funded PhDs

PhD title Research Group Supervisor Closing Date
PhD Studentships in Antennas, Nano-Electromagnetics and Smart Materials Antennas & Electromagnetics 30/11/2016 Closed
PhD Studentships in audio-visual intelligent sensing Computer Vision 15/11/2016 Closed
PhD Studentship in Deep Learning for Computer Vision Computer Vision 23/09/2016 Closed
PhD projects in Semantic Audio and Music Informatics Centre for Digital Music (C4DM) 31/08/2016 Closed
PhD Studentship in Dynamic Adaptive Automated Software Engineering Computer Vision 31/08/2016 Closed
PhD Studentship in Semantics and Verification of Heterogeneous Programs Theoretical Computer Science (Theory) 24/07/2016 Closed
PhD Studentship in “Personal Data, Thinking Inside the Box” Cognitive Science 15/05/2016 Closed
PhD Studentships in Electronic Engineering and Computer Science In one of the Research Groups 06/05/2016 Closed
PhD Studentship in Large-Scale Online Video Service Measurements Networks 13/04/2016 Closed
PhD Studentship in battery technology for electric vehicles Antennas & Electromagnetics 01/03/2016 Closed
PhD Studentship in Machine Listening / Music Informatics at the Centre for Digital Music Centre for Digital Music (C4DM) 29/02/2016 Closed
PhD Studentship in collaboration with Solid State Logic (SSL) “Real-Time Tracking for Spatial Audio Centre for Digital Music (C4DM) 01/02/2016 Closed
PhD Studentship in collaboration with Solid State Logic (SSL) “Tools for Object-Oriented Mixing” Centre for Digital Music (C4DM) 01/02/2016 Closed
PhD Studentship in Semantic Audio Centre for Digital Music (C4DM) 05/01/2016 Closed
PhD Studentship in Design of MIMO Antennas with Characteristic Modes Antennas & Electromagnetics 30/11/2015 Closed
PhD Studentship in collaboration with OMRON “Fine-Grained Sketch-Based Image Retrieval” Computer Vision 13/09/2015 Closed
PhD Studentship in Active & Nonlinear Metamaterials Antennas & Electromagnetics 28/08/2015 Closed
PhD Studentship in Semantic Audio and Intelligent Music Production Centre for Digital Music (C4DM) 28/08/2015 Closed
Two PhD Studentship in Semantic Audio and Music Informatics Centre for Digital Music (C4DM) 31/07/2015 Closed
PhD Studentships in audio-visual intelligent sensing Centre for Intelligent Sensing 08/06/2015 Closed
PhD Studentships in audio-visual intelligent sensing Centre for Intelligent Sensing 08/06/2015 Closed
PhD Studentship in Intelligent Machine Music Listening Centre for Digital Music (C4DM) 01/05/2015 Closed
PhD Studentship in Large-Scale Dynamic Graph Sensing Networks 01/05/2015 Closed
PhD Studentship in Eye-Movement Analysis Computer Vision 01/04/2015 Closed
1 PhD studentship in “Software-defined networking” Networks 18/01/2015 Closed
PhD Studentship in Machine transcription of wildlife bird sound scenes Centre for Digital Music (C4DM) 12/01/2015 Closed
PhD Studentship in machine learning applied to sound synthesis and media content creation Centre for Digital Music (C4DM) 16/12/2014 Closed
EPSRC PhD CASE STUDENTSHIP Antennas & Electromagnetics 19/09/2014 Closed
Experimenting with network measurements and evaluating user QoE Networks 18/07/2014 Closed
PhD Studentship in Machine Intelligence Risk and Information Management (RIM) 16/07/2014 Closed
Media & Arts Technology Programme 2014 Cognitive Science 07/07/2014 Closed
AHRC Doctoral Studentship in Computational Musicology Cognitive Science 01/07/2014 Closed
PhD Studentship in Rhythm perception and analysis Centre for Digital Music (C4DM) 10/05/2014 Closed
PhD studentship: “Machine learning for bioinformatics: integrating multi ‘omics and clinical data” Risk and Information Management (RIM) 16/04/2014 Closed
PhD Studentships in Electronic Engineering and Computer Science In one of the Research Groups 11/04/2014 Closed
PhD in Cognitive Robotics Cognitive Science 11/04/2014 Closed
PhD Studentship in Program Analysis Theoretical Computer Science (Theory) 11/04/2014 Closed
colour vision technology for reflective electrochromic display Multimedia & Vision (MMV) 31/03/2014 Closed
semantic image understanding and 3D reconstruction Multimedia & Vision (MMV) 31/03/2014 Closed
Resource Constrained Intelligent Sensing Risk and Information Management (RIM) 26/02/2014 Closed
Computer Vision based Analysis of Human Behaviour - Life Sciences Institute Multimedia & Vision (MMV) 01/02/2014 Closed
Geometric Modelling of 3D Point-Cloud Data Computer Vision 01/02/2014 Closed
Music Perception & Cognition Cognitive Science 01/02/2014 Closed
Integrated Decision Making in Trauma Medicine Risk and Information Management (RIM) 01/02/2014 Closed
Analysis of high dimensional multi ‘omics data for translational bioinformatics Computer Vision 01/02/2014 Closed
Signal Processing and Data Mining Tools for the Analysis of Musical Evolution Centre for Digital Music (C4DM) 01/02/2014 Closed
Bio-inspired Cognitive Architectures for Open-Ended Learning Cognitive Science 01/02/2014 Closed
Audio signal analysis Centre for Digital Music (C4DM) 01/02/2014 Closed
Large scale analysis of forensic data Multimedia & Vision (MMV) 01/02/2014 Closed
Large-scale Internet analytics Networks 04/12/2013 Closed
Visualizing the structure of information streams for aiding diagnosis/troubleshooting Networks Closed
Fully funded PhD studentship on video compression with specific focus on the emerging HEVC standard Multimedia & Vision (MMV) Closed
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PhD Studentships in Antennas, Nano-Electromagnetics and Smart Materials


Application closing date: 30/11/2016 Closed
Start date: 01/02/2017
Research group: Antennas & Electromagnetics
Duration: 3 years Years Funding available

Applications are invited for five fully-funded PhD studentships within the Antennas and Electromagnetics Group at Queen Mary University of London, in the emerging field of Antennas, Nano-Electromagnetics and Advanced Materials. The funding is partially supported by IET AF Harvey Research Prize recently awarded to Professor Yang Hao. All nationalities are eligible to apply for this studentship, which will start in January 2017. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,296 per annum.

Candidates must have a 2:1 degree or equivalent, and/or a good MSc Degree in Electronic Engineering, Physics, Material Science or a related discipline. The ideal candidate should be creative and motivated in the studying of future antenna technology and advanced materials. Knowledge of fundamental electromagnetics, microwave and THz engineering and nano-technologies are desirable and will be an advantage. We also look for candidates who are interested in subject areas related to big data science, synthetic biology and artificial intelligence. In addition, applicants will have at least good knowledge of the common programming languages such as C/C++, Python, Jave, Matlab. Analytical and good communication skills are also welcome.

The PhD supervisor will be Professor Yang Hao. The project will be based in the School of Electronic Engineering and Computer Science (EECS), and the student will join a group of 51 full-time PhD students, post-doctoral researchers and academics in the Antennas and Electromagnetics Group (http://antennas.eecs.qmul.ac.uk/), a world-leading multidisciplinary research group in the field of computational electromagnetics, microwave metamaterials, transformation electromagnetics, antennas and radio propagation for body centric wireless networks, active antennas for millimeter/sub-millimeter applications and photonic integrated antennas, graphene and nanomicrowave. Informal enquiries about the studentship can be made by email to prof. Hao (y.hao@qmul.ac.uk).

To apply, please follow the on-line process at (www.qmul.ac.uk/postgraduate/applyresearchdegrees/); click on the list of Research Degree Subjects, select ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. excerpt of final year dissertation or published academic paper). More details can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for the applications is 30/11/2016.

Interviews are expected to take place the week of 09/01/2017.

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PhD Studentships in audio-visual intelligent sensing


Application closing date: 15/11/2016 Closed
Start date: 09/01/2017
Research group: Computer Vision
Duration: 3 years Years Funding available

Applications are invited for 2 (two) PhD Studentships to undertake research in the areas of computer vision and audio processing for people monitoring in multi-camera multi-microphone environments, and will be part of an interdisciplinary project on mobile audio-visual monitoring for smart interactive and reactive environments. The Studentships (to be started in or after January 2017) are part of an interdisciplinary project between the Centre for Intelligent Sensing (http://cis.eecs.qmul.ac.uk) at Queen Mary University of London (QMUL) and the Centre for Information Technology (http://ict.fbk.eu) at the Fondazione Bruno Kessler (FBK), Trento, Italy.

The Project will focus on methods for people tracking, activity recognition, acoustic scene analysis, behaviour analysis, distant-speech recognition and understanding applied to individuals as well as groups. Such information will enable learning 'patterns of usage' of the environment, and patterns can in turn be used to adapt and optimise the sensing accordingly.

Each PhD student will spend approximatively 50% of their time in London and 50% of their PhD time in Trento and will have access to state-of-the-art audio-visual laboratories, including robotic sensors, a multi-camera multi-microphone installation at a large open hallway and a smart home facility equipped with cameras and microphones.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and should have good programming experience, in particular of C/C++ language and of MATLAB environment. Previous knowledge of Signal Processing is a requirement. Previous knowledge of Computer Vision or Deep Learning/Machine Learning or Robotic Sensing or Audio Signal Processing and/or Speech Recognition is desired, but not required.

The studentships will be based at Centre for Intelligent Sensing in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Professor Andrea Cavallaro (http://www.eecs.qmul.ac.uk/~andrea/) and Dr Oswald Lanz (https://tev.fbk.eu/people/profile/lanz) or, depending on the type of the PhD project chosen by the candidate, Dr Maurizio Omologo (http://shine.fbk.eu/people/omologo). To apply please follow the on-line process at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting Electronic Engineering or Computer Science in the A-Z list of research opportunities and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest (no more than 500 words or one side of A4 paper) should state whether you are interested in a computer vision PhD project, or an audio processing PhD project, or an audio-visual processing PhD project. Moreover, your Statement of Research Interest should answer two questions: Why are you interested in the proposed area? What is your experience in the proposed area? In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php .

Informal enquiries can be made by email to Professor Andrea Cavallaro (a.cavallaro@qmul.ac.uk).

The closing date for the applications is 15 November 2016.

Interviews are expected to take place during the week commencing 28 November 2016.

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PhD Studentship in Deep Learning for Computer Vision


Application closing date: 23/09/2016 Closed
Start date: 03/10/2016
Research group: Computer Vision
Duration: 3 years Years Funding available

The Computer Vision Research Group within the School of Electronic Engineering and Computer Science, QMUL, invites applications for one fully-funded PhD studentship to work on an industrial sponsored project for deep learning based video analysis for object detection and identification. This is a three years collaborative project between Queen Mary Computer Vision Group and our industrial partners Seequestor and Vision Semantics.

A central challenge to real-time object detection, identification and re-identification “in-the-wild” is obtaining a rich representation that is both salient and unique for individual object description, also robust to changes in viewing conditions and occlusion. Conventional approaches address this problem by learning hand-crafted low-level visual features. A fundamental problem with low-level feature learning is that many do not perform consistently cross disjoint views over time with large appearance disparities. Moreover, model learning is subject to sparsely labelled clean data in the presence of overwhelming unlabelled and noisy data, which poses significant challenges to deep learning frameworks. This project will investigate novel deep learning techniques for video analysis given sparely labelled data together with large quantity of unlabelled data. In particular, the successful candidates will develop real-time deep models for urban space object detection and identification “in-the-wild” with close collaboration with industrial partners.

All nationalities are eligible to apply for this studentship, which will ideally start in October/November 2016. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,296 per annum.

Candidates are normally expected to have a 2.1 or 1st class BA/BSc honors degree and a Master degree in Computer Science, Mathematics or related discipline from a UK University or an equivalent standard from an overseas university. The successful candidate must have a strong programming background in C++, Python and Matlab, as well as good analytical and communication skills. The student is expected to work as part of a team and independently, and to prepare clear reports and publish peer-reviewed papers. Some knowledge of and experience on machine learning and deep learning programming environments are highly desirable although not mandatory.

Informal enquiries can be made by email to Professor Shaogang Gong (s.gong@qmul.ac.uk).

For more information and to apply, please visit: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 23rd September 2016.

Interviews are expected to take place shortly after. The position remains open until suitable candidates are appointed.

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PhD projects in Semantic Audio and Music Informatics


Application closing date: 31/08/2016 Closed
Start date: 03/10/2016
Research group: Centre for Digital Music (C4DM)
Duration: 3 years Years Funding available

Applications are invited 1 fully-funded PhD studentship, allied to the 5 year EPSRC and Digital Economy funded Programme Grant: Fusing Audio and Semantic Technologies for Intelligent Music Production and Consumption (FAST-IMPACt or FAST - see www.semanticaudio.ac.uk).

FAST-IMPACt aims to answer questions such as: How can next generation web technologies (Ontologies, Linked Data, Metadata) combined with music content analysis in the studio bring new value and functionality to producers, creators, consumers and intermediaries of music content? And how will both ends of the music value chain benefit from more engaging interactions (enhanced productivity, increased enjoyment and immersion) while creating or consuming music? And can intermediaries add value with semantically enhanced services?

Helping us pursue this vision are national and international partners from academia and industry, including BBC R&D, Abbey Road, Solid State Logic, International Audio Labs and more.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Sound & Music Computing or equivalent. Candidates should be confident in digital signal processing and/or machine learning, and have programming experience in, e.g. MATLAB, Mathematica, Python, Java, C++ or similar. Experience in research and a track record of publications is very advantageous. Formal music training or sound engineering experience is also advantageous.

Positions are available immediately. Only 1 place is available with full fees and stipend; but additional positions may be available for self-funded or part-funded applicants. Please apply online via the Queen Mary University of London application system, quoting the specific project(s) of interest. Enquiries may be addressed to mark.sandler@qmul.ac.uk.

Projects titles are below. Fuller details of each of the projects below are available at http://tinyurl.com/jye2x69

[SAMI1] Studio Science: improving feature extraction in the studio; delivering new experiences to the consumer

[SAMI2] Enhancing the music listening experience

[SAMI3] Song level audio features for navigating large music collections

SAMI4] Note level audio features for understanding and visualising musical performance

[SAMI5] Audio features for MIR based on human hearing physiology and neuroscience and on acoustics

[SAMI6] Compression of individual instrument stems for compact multi-track audio formats

The closing date for the applications is 31/08/2016

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PhD Studentship in Dynamic Adaptive Automated Software Engineering


Application closing date: 31/08/2016 Closed
Start date: 03/10/2016
Research group: Computer Vision
Duration: 3 years Years Funding available

The Operational Research Group within the School of Electronic Engineering and Computer Science, QMUL, invites applications for a fully funded PhD studentship to work on a project funded by the Engineering and Physical Sciences Research Council.

DAASE (Dynamic Adaptive Automated Software Engineering) is a five site EPRSC funded project involving QMUL, University College London, The University of Birmingham, The University of Stirling and The University of York. Our industrial partners include Berner and Mattner, BT Laboratories, Ericsson, GCHQ, Honda Research Institute Europe, IBM, Microsoft Research and Motorola UK.

Current software development processes are expensive, laborious and error prone. They achieve adaptivity at only a glacial pace, largely through enormous human effort, forcing highly skilled engineers to waste significant time adapting many tedious implementation details. Often, the resulting software is equally inflexible, forcing users to also rely on their innate human adaptivity to find "workarounds". Yet software is one of the most inherently flexible engineering materials with which we have worked, DAASE seeks to use computational search as an overall approach to achieve the software's full potential for flexibility and adaptivity. In so-doing we will be creating new ways to develop and deploy software. This is the new approach to software engineering DAASE seeks to create. It places computational search at the heart of the processes and products it creates and embeds adaptivity into both. DAASE will also create an array of new processes, methods, techniques and tools for a new kind of software engineering, radically transforming the theory and practice of software engineering. The successful candidate will pursue a course of research investigating the application of computational search methods, such as evolutionary computation and meta/hyper-heuristics, to software engineering challenges with a focus on real-world applications.

DAASE is a highly collaborative project. The successful candidate will have opportunities to visit and work with industrial and other partners and to be fully engaged with the international community through conferences, workshops and other networking activities. This will enhance training and development and open new opportunities for collaboration and intellectual development. Students will also have the opportunity to engage with researchers within the group working on another EPSRC funded project, OR-MASTER, which focuses on the development of mathematical models, algorithms, and computer applications to optimise problems occurring in the airline industry.

All nationalities are eligible to apply for this studentship, which will ideally start in September/October 2016. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,296 per annum.

Candidates are normally expected to have a 2.1 or 1st class honors degree in Computer Science, Mathematics, Operations Research or related discipline, from a UK University or an equivalent standard from an overseas university. The successful candidate must have a strong programming background, as well as good analytical and communication skills. The student is expected to work as part of a team and independently, and to prepare clear reports and research papers. An understanding of mathematical optimization techniques, heuristic and hyper-heuristic search is highly desirable although not mandatory.

Informal enquiries can be made by email to Dr. John Drake (j.drake@qmul.ac.uk) who will supervise the project alongside Prof Edmund Burke.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the DAASE project described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31st August 2016

Interviews are expected to take place shortly after

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PhD Studentship in Semantics and Verification of Heterogeneous Programs


Application closing date: 24/07/2016 Closed
Start date: 03/10/2016
Research group: Theoretical Computer Science (Theory)
Duration: 3 years Years Funding available

Applications are invited for a fully-funded PhD studentship within the Theory Group at Queen Mary University of London, as part of a project which aims to develop a unified semantic framework for heterogeneous software systems and apply it to compositional software compilation and verification. Cloud computing and heterogeneous computing are widely acknowledged to dominate the software landscape in the foreseeable future. The recent work on System-Level Games provides a semantic framework for modelling low-level code interactions involving resources shared between a program and its environment. This project will apply the framework for deriving compositional analysis techniques for the compilation and verification of heterogeneous programs.

All nationalities are eligible to apply for this studentship, which will start in October 2016. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,057 per annum. Candidates must have a 2:1 degree or equivalent, and/or a good MSc Degree, in Computer Science or a related discipline. The ideal candidate should be creative and motivated in the studying of semantics and verification of programming languages. Good coding skills will be an advantage, and applicants will have at least good knowledge of programming languages such as C/C++, Java, Python, OCaml. Analytical and good communication skills are also welcome. The PhD supervisor will be Dr Nikos Tzevelekos. The project will be based in the School of Electronic Engineering and Computer Science (EECS), and the student will join a world-leading centre for research on logical methods for reasoning about computer systems in the Theory Group (http://theory.eecs.qmul.ac.uk/). The position will be integrated in the EPSRC project "System-Level Game Semantics: A unifying framework for composing systems", which is in collaboration with the University of Birmingham. Informal enquiries about the studentship can be made by email to Dr Tzevelekos (nikos.tzevelekos@qmul.ac.uk).

To apply, please follow the on-line process at www.qmul.ac.uk/postgraduate/applyresearchdegrees/ click on the list of Research Degree Subjects, select ‘Computer Science’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. excerpt of final year dissertation or published academic paper). More details can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for the applications is 24/07/2016.

Interviews are expected to take place the week of 25 July 2016.

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PhD Studentship in “Personal Data, Thinking Inside the Box”


Application closing date: 15/05/2016 Closed
Start date: 04/01/2016
Research group: Cognitive Science
Duration: 3 years Years Funding available

Applications are invited for a fully funded PhD studentship, allied to the collaboration agreement between QMUL (CIS) and University of Genoa (DITEN). The research is in the general area of Personal Data & Human-Data Interaction. There is a critical need to provide technologies that enable alternative practices, so that individuals can participate in the collection, management and consumption of their personal data. In this studentship, we will investigate the systems, data, and applications for having a Databox, a personal networked device that collates and mediates access to personal data, allowing us to recover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data. The Databox can act as a hub for individuals’ mobility and physical activity data, social media data, third party tracking, and ambient data from the environment.

All nationalities are eligible to apply for this studentship, which will start in January 2016. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, HCI or equivalent. Candidates should be confident in data measurement and API programming, and/or machine learning, and have programming experience in, e.g. Python, R/MATLAB or similar. Prior experience with data collection and processing, analysis, and visualisation is welcome. Experience in research and a track record of publications is very advantageous. Although the skills and ability to perform conventional data collection and analysis methods is a must, the candidate should be able to think independently and creatively about the way they would envision a novel and user-centric personal data ecosystem.

Informal enquiries can be made by email to Dr Hamed Haddadi (hamed.haddadi@qmul.ac.uk) who will lead the PhD supervision.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 15/09/15.

Interviews are expected to take place during the week of 20/09/15.

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PhD Studentships in Electronic Engineering and Computer Science


Application closing date: 06/05/2016 Closed
Start date: 03/10/2016
Duration: 3 years Years Funding available

The School of Electronic Engineering and Computer Science at Queen Mary University of London has a number of fully-funded studentships for exceptional PhD applicants to join our school. Applications will be considered in any area of Electronic Engineering and/or Computer Science matching our research interests. For general information on research in EECS and our research groups, see http://www.eecs.qmul.ac.uk/research/, and for a list of potential research projects see http://www.eecs.qmul.ac.uk/phd/research-topics/projectideas. Applicants may also propose their own topics, but they must be able to find a suitable supervisor who is willing to supervise the project. Prospective applicants are advised to contact potential supervisors to discuss project ideas and to ascertain whether the supervisor is eligible for one of these studentships.

Applicants of all nationalities are eligible to apply for these studentships, which are planned to start in Autumn 2016. The studentship is for three years, and covers tuition fees as well as a tax-free stipend starting at £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, and/or a good MSc degree in Computer Science, Electronic Engineering, or a related discipline. Experience in research and a track record of publications are advantageous but not a strict requirement.

The project will be based in the School of EECS, and the student will join a group of over 300 full-time PhD students, post-doctoral researchers and academics in the EECS. For general enquiries contact Mrs Melissa Yeo m.yeo@qmul.ac.uk (administrative enquiries) or Dr Simon Dixon s.e.dixon@qmul.ac.uk (academic enquiries).

To apply for one of these studentships, please follow the on-line process at www.qmul.ac.uk/postgraduate/applyresearchdegrees/; click on the list of Research Degree Subjects, select ‘Electronic Engineering’ or ‘Computer Science’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page. If you have a particular supervisor in mind, make sure that you indicate this person on the application form.

In your 'Research Proposal' you should describe the topic or topics that you are interested in researching (preferably after discussing these with potential supervisors), and answer the two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. an excerpt from your final year dissertation or a published academic paper). More details on the application process can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for applications is 6/5/2016.

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PhD Studentship in Large-Scale Online Video Service Measurements


Application closing date: 13/04/2016 Closed
Start date: 03/10/2016
Research group: Networks
Duration: 3 years Years Funding available

Applications are invited for a fully-funded PhD studentship within the Networks Research Group at Queen Mary University of London, in the field of large-scale Internet/Web measurements. The research involved in this project pertains to the (fast-changing) area of online video delivery, especially platforms used to share user-generated content for which copyright issues may occur. This is a topic that the research community has little understanding of outside of the major players such as Youtube. The project intends to address this deficiency to answer a wide range of questions in the field, for example:

• Which video hosts provide the greatest amount of content? How do their infrastructures operate compared to other more established services like YouTube and Netflix?

• Who are the different stakeholders involved in distributing various types of content? Can they be monitored to understand incentives and business models?

• What are the temporal dynamics of how content is added and removed? What events trigger the injection or removal of content?

• How do these stakeholders join and leave the “market” of online content delivery?

• Is there collusion between stakeholders? If so, what are their incentives?

To answer these questions, the project will take an empirical approach, devising and deploying novel measurement techniques for monitoring how various forms of online content are distributed. The specifics of how this is done are open to design by the successful candidate, and the above questions represent only a small set of examples. The high-level vision of the project is to contribute new techniques and data to the community to provide a deeper understanding of the online video ecosystem. However, there is significant scope to tailor the research to the interests and skills of the successful candidate.

All nationalities are eligible to apply for this studentship, which will start in Autumn 2016. The studentship is for three years, and covers student fees as well as a tax-free stipend starting at £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, and/or a good MSc degree in Computer Science, Electronic Engineering, or a related discipline. Knowledge of Internet/Web technologies is desirable, as is the ability to programme to a high standard (e.g. Java, Python). Experience in research and a track record of publications is advantageous but not a strict requirement.

The PhD supervisors will be Dr. Gareth Tyson and Prof. Steve Uhlig. The project will be based in the School of EECS, and the student will join a group of around 50 full-time PhD students, post-doctoral researchers and academics in the Networks Research Group (http://networks.eecs.qmul.ac.uk/). Informal enquiries about the studentship can be made by email to Dr Tyson (gareth.tyson@qmul.ac.uk). To apply, please follow the on-line process at (www.qmul.ac.uk/postgraduate/applyresearchdegrees/); click on the list of Research Degree Subjects, select ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. excerpt of final year dissertation or published academic paper). More details can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for the applications is 13/04/2016.

Interviews are expected to take place in the last week of April, 2016.

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PhD Studentship in battery technology for electric vehicles


Application closing date: 01/03/2016 Closed
Start date: 01/04/2016
Research group: Antennas & Electromagnetics
Duration: 3 years Years Funding available

Applications are invited for a fully-funded PhD studentship to investigate novel battery technologies for Electric Vehicles (EVs). Prolonging and predicting the life cycle of electrochemical battery pack has been the subject of intensive studies toward the large-scale use of EVs (both for Battery EVs and Plug-in Hybrid EVs). In commercial EVs, a group of batteries are bundled together as a battery pack to provide the required power for Electric motor of EVs. The life-cycle prediction of a battery pack is quite challenging as the electrochemical dynamics and thermal characteristics of e.g. a single Li-ion battery cell, is different from the other cells. Random variability and aging propagation of a cell can substantially reduce the lifetime of the whole pack. This project investigates the novel use of antenna/wireless system in suppressing the aging propagation by providing the status of any failing cell and intelligently reconfigure the battery system. The other large interest of such technology would be for vehicle-to-grid (V2G) applications where the vehicular battery is used for storing electrical energy generated by home renewables. The student will have access to a fully-equipped dSPACE real-time laboratory for battery and power electronics hardware-in-the-loop (HIL) simulation and other specialized commercial software for Antenna/EM design. There is also support within the group for travel to major international conferences.

All nationalities are eligible to apply for this studentship, which will start in Spring 2016. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Electrical and Electronic Engineering, Power Engineering, Control Engineering, Materials Science, or related areas. Candidates should have good knowledge of power electronics (power converters) and vehicular battery technologies as well as extensive experience in modelling and simulation with MATLAB and real-time systems. With regards to the multidisciplinary nature of this project, it is highly desirable that the candidate have significant knowledge of or experience with RF wireless subsystems along with microsystems/material sciences.

The main PhD supervisor will be Dr. Kamyar Mehran (Antennas & Electromagnetics Group). Over the course of the PhD the student will have ample opportunity to collaborate with other members of the Group, as well as a number of partners at institutes throughout the UK and internationally. Informal enquiries about the studentship can be made by email to Dr. Mehran (k.mehran@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 01/03/16. Interviews are expected to take place during the week of 14/03/16.

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PhD Studentship in Machine Listening / Music Informatics at the Centre for Digital Music


Application closing date: 29/02/2016 Closed
Start date: 03/10/2016
Research group: Centre for Digital Music (C4DM)
Duration: 3 years Years Funding available

Applications are invited for a fully-funded PhD studentship within the Centre for Digital Music (C4DM) at Queen Mary University of London, in the fields of machine listening / music informatics. PhD applications are invited that address one of the following topics:

• Recognition and Separation of Musical Instruments in Polyphonic Audio: The goal of this project is to develop computational techniques for automatic identification/separation of multiple instruments in music signals, as well as instrument assignment – i.e. assigning detected notes to a specific instrument.

• Sound Event Detection in Multisource Environments: This project will focus on detecting sound events (also called acoustic events) from everyday sound scenes. The successful candidate will research and develop computational methods suitable for detecting overlapping sound events in noisy and complex acoustic environments.

• Music Language Models for Audio Analysis: The goal of this project is to develop language models for polyphonic music and integrate them to systems for analysing music signals (e.g. automatic music transcription, chord estimation), in a similar way that spoken language models are combined with acoustic models for automatic speech recognition.

All nationalities are eligible to apply for this studentship, which will start in Autumn 2016. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, and/or a good MSc Degree in Computer Science, Electronic Engineering, Music/Audio Technology, or a related discipline. Knowledge of digital signal processing and/or machine learning is desirable, as well as programming experience in, e.g. MATLAB, Python, Java, C++ or similar. Experience in research and a track record of publications is advantageous. Formal music training is also desirable for the 3rd topic. There is scope to tailor the research to the interests and skills of the successful candidate.

The PhD supervisor will be Dr Emmanouil Benetos. The project will be based in the School of EECS, and the student will join a group of around 60 full-time PhD students, post-doctoral researchers and academics in the Centre for Digital Music (C4DM - http://c4dm.eecs.qmul.ac.uk/), a world-leading multidisciplinary research group in the field of Music & Audio Technology. Informal enquiries about the studentship can be made by email to Dr Benetos (emmanouil.benetos@qmul.ac.uk).

To apply, please follow the on-line process at (www.qmul.ac.uk/postgraduate/applyresearchdegrees/); click on the list of Research Degree Subjects, select ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’, and follow the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. excerpt of final year dissertation or published academic paper). More details can be found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply

The closing date for the applications is 29/02/2016.

Interviews are expected to take place in March 2016.

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PhD Studentship in collaboration with Solid State Logic (SSL) “Real-Time Tracking for Spatial Audio


Application closing date: 01/02/2016 Closed
Start date: 01/04/2016
Research group: Centre for Digital Music (C4DM)
Duration: 3.5 Years Funding available

Applications are invited for a fully-funded PhD studentship to conduct research into Real-Time Tracking for Spatial Audio Object-based Mixing. This research will investigate and prototype tools to combine object-oriented mixing with real-time listener tracking to develop new spatial audio applications. The aim of the project is to overcome the limitations of current channel-based mixing environments. The candidate will work in very close collaboration with SSL.

This studentship is funded by the EPSRC and SSL. Funding is available for 3.5 years and will cover student fees and a tax-free stipend starting at £21,900 per annum.

UK residents are eligible to apply for this studentship as defined for the purpose by EPSRC (https://www.epsrc.ac.uk/skills/students/help/eligibility). The position will start as soon as possible before April 2016. Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Physics or Mathematics. Experience of programming in e.g. C/C++ Matlab or Python is essential. Knowledge of audio signal processing, spatial audio rendering, music information retrieval and audio production are highly desirable.

The PhD supervisor will be Dr. George Fazekas. The student will also work closely with team from SSL led by Dr. Enrique Perez. The successful candidate will be part of the Centre for Digital Music (C4DM) that is one of the most renowned digital music research labs internationally. Please see http://c4dm.eecs.qmul.ac.uk/ for background. Informal enquiries can be made to Dr. George Fazekas (g.fazekas@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is Mon. 1 February 2015.

Interviews are expected to take place shortly afterwards.

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PhD Studentship in collaboration with Solid State Logic (SSL) “Tools for Object-Oriented Mixing”


Application closing date: 01/02/2016 Closed
Start date: 01/04/2016
Research group: Centre for Digital Music (C4DM)
Duration: 3.5 Years Funding available

Applications are invited for a fully-funded PhD studentship, to conduct research into Tools for Object-Oriented Mixing. The aim of the research is to investigate and prototype an object-oriented mixing environment where content is interpreted using audio, metadata and semantic analysis to infer meaningful audio objects and provide new interfaces for creating application specific mixes. The candidate will work in very close collaboration with SSL. This studentship is funded by the EPSRC and SSL. Funding is available for 3.5 years and will cover student fees and a tax-free stipend starting at £21,900 per annum.

UK residents are eligible to apply for this studentship as defined for the purpose by EPSRC (https://www.epsrc.ac.uk/skills/students/help/eligibility). The position will start as soon as possible before April 2016. Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Physics or Mathematics. Experience of programming in e.g. C/C++ Matlab or Python is essential. Knowledge of audio signal processing, music information retrieval, audio production, metadata and semantic analysis are highly desirable.

The PhD supervisor will be Dr. George Fazekas. The student will also work closely with team from SSL led by Dr. Enrique Perez. The successful candidate will be part of the Centre for Digital Music (C4DM) that is one of the most renowned digital music research labs internationally. Please see http://c4dm.eecs.qmul.ac.uk/ for background. Informal enquiries can be made to Dr. George Fazekas (g.fazekas@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is Mon. 1 February 2015.

Interviews are expected to take place shortly afterwards.

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PhD Studentship in Semantic Audio


Application closing date: 05/01/2016 Closed
Start date: 01/02/2016
Research group: Centre for Digital Music (C4DM)
Duration: 3 years Years Funding available

Applications are invited for a fully funded PhD studentship undertaking research into Semantic Audio within the context of a European Union Horizon 2020-funded project Audio Commons.

The project brings the success of Creative Commons to the world of audio. It aims to address several issues faced by the creative industries when using musical as well as non-musical audio material on the Web and aims to develop novel methods enabling the creation, access, retrieval and reuse of audio material in innovative new ways.

Audio Commons is a joint project combining the strength of 3 academic and 3 industry partners; UPF, Spain, Queen Mary University of London, UK, University of Surrey, UK, AudioGaming, France, Waves, Israel and Jamendo Music, Luxembourg.

The student will be a member of the Centre for Digital Music at Queen Mary University School of Electronic Engineering and Computer Science. The student will be supervised by Dr. George Fazekas.

The successful candidate will develop algorithms that combine audio signal processing for content analysis with semantic web technologies for gathering and analysing contextual information related to audio recordings. The work will involve the development technologies supporting the Audio Commons Ecosystem, developing novel signal processing and machine learning algorithms for content annotation, and conducting user studies to evaluate application specific prototypes, for instance novel tools for sound design or audio retrieval, developed in collaboration with the project's industry partners.

All nationals are eligible to apply for this studentship, which will start in February 2016. The studentship is for 3 years and covers student fees as well as a tax-free stipend of £19,200 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Physics or Mathematics. Candidates must be confident in signal processing and web technologies and have programming experience. Experience in Semantic Web (RDF, OWL, SPARQL) and machine learning would be an advantage, as would previous experience in research and a track record of publications. Informal enquiries can be made by email to Dr George Fazekas (g.fazekas@qmul.ac.uk, http://www.eecs.qmul.ac.uk/~gyorgyf/) To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 5th. January 2016.

Interviews are expected to take place during the week of 11th January 2016

.

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PhD Studentship in Design of MIMO Antennas with Characteristic Modes


Application closing date: 30/11/2015 Closed
Start date: 04/01/2016
Research group: Antennas & Electromagnetics
Duration: 3 years Years Funding available

Applications are invited for a full PhD Studentship starting in January 2015 to undertake research in the emerging field of MIMO (multiple-input and multiple-output) antenna design for 5G wireless systems using Characteristic Modes Analysis (CMA) based Theory of Characteristic Modes (TCM). The studentship will include both theoretical and applied research in the well-equipped Antennas Lab with over 40 years experience at Queen Mary University of London.

The characteristic mode analysis (CMA) supported by theory of characteristic mode (TCM) becomes a efficient tool to design antenna structure embed on different platforms, especially multiple-input multiple-output (MIMO antennas. TCM was proposed by Garbacz and further developed by Harrington and Mautz. It makes it possible to obtain full characteristics of a radiating body with arbitrary geometry. It not only can apply on conductors, but also dielectric and magnetic materials. Based on insights in the fundamental resonance characteristics of antenna geometries and structures to be mounted on, excitations locations and modes of the antenna can be optimized. Another powerful feature of TCM is that multiple characteristic modes at a given frequency facilitate orthogonal radiation patterns, which effectively enables MIMO antennas for 5G wireless systems.

The student will be based in the School of Electronic Engineering and Computer Science (www.eecs.qmul.ac.uk) at Queen Mary University of London, and will be a member of the Antennas and Electromagnetics research group (http://antennas.eecs.qmul.ac.uk), which has enjoyed a distinguished reputation for innovation for over 40 years.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Engineering, Physics or Mathematics. Candidates must be confident in antenna engineering and EM modelling tools. Previous experience in antenna design will be a key advantage.

Informal enquiries can be made by email to Dr Yue Gao (yue.gao@qmul.ac.uk).

This studentship is available to candidates of all nationalities. It is funded by the university and MPI-QMUL information system research centre for 3.5 years will cover student fees and a tax-free stipend starting at £15,209 per annum.

To apply, please follow the online process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 30 November 2015.

Interviews are expected to take place during the week of 01 December 2015.

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PhD Studentship in collaboration with OMRON “Fine-Grained Sketch-Based Image Retrieval”


Application closing date: 13/09/2015 Closed
Start date: 02/11/2015
Research group: Computer Vision
Duration: 3 years Years Funding available

Applications are invited for a fully-funded PhD studentship, to conduct research into Sketch-Based Image Retrieval (SBIR). The project will develop new perspectives towards SBIR, based on recent developments from free-hand sketch analysis, deep neural networks, and visual attribute mining and detection. In particular, it will study how sketches can be used as an effective means of retrieval images from a large dataset in a fine-grained manner, i.e., find a specific type of shoes by sketching it other than describing in text or providing a picture. The candidate will work in very close collaboration with OMRON. All nationalities are eligible to apply for this studentship, which will start in September/October 2015. This studentship, jointly funded by QMUL and OMRON, is for 3 years and will cover student fees and a tax-free stipend starting at £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Physics or Mathematics. Experience of programming in e.g. Matlab, C/C++, or Python is essential. Knowledge of machine learning, image retrieval and sketch analysis would be an advantage.

The PhD supervisor will be Dr. Yi-Zhe Song. The student will also work closely with team from OMRON, led by Dr. Xiang Ruan. The project will be part of ongoing research into understanding human sketches, which is one of the most rapidly growing research topics in computer vision with potential widespread applications in the short-term. The successful candidate will be part of the Vision group that is one of the most renowned Computer Vision research labs internationally. Please see http://www.eecs.qmul.ac.uk/~yzs and http://vision.eecs.qmul.ac.uk/ for background. Informal enquiries can be made to Dr. Yi-Zhe Song (yizhe.song@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 13th September 2015.

Interviews are expected to take place during September 2015.

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PhD Studentship in Active & Nonlinear Metamaterials


Application closing date: 28/08/2015 Closed
Start date: 01/10/2015
Research group: Antennas & Electromagnetics
Duration: 3 years Years Funding available

Applications are invited for a fully-funded PhD studentship with the Antennas and Electromagnetics Group (AEG), to investigate novel active and nonlinear metamaterials. The candidate will explore methods for enabling exciting electromagnetic phenomena, such as artificial magnetic conductors and materials with near-zero or negative properties. Most current iterations of these metamaterials suffer from high dispersion and loss, which make them unsuitable for practical applications. A major emphasis will be the design of broadband and low loss structures, particularly with reconfigurable capabilities, using technologies such as non-Foster circuits. This will feed into the development of a range of useful applications ranging from microwave, to millimetrewave and terahertz (THz) bands. The student will have access to a range of commercial software tools, including CST Microwave Studio, Keysight ADS and COMSOL, in addition to the AEG’s world-class facilities for measurements ranging from DC to THz. There is also support within the group for travel to major international conferences.

All nationalities are eligible to apply for this studentship, which will start in Autumn 2015. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Electronic Engineering, Physics, Materials Science, or related areas. Candidates should have good knowledge of the physics of electromagnetics, as well as experience with electromagnetic modelling using commercial tools or self-programmed software. It is highly desirable that the candidate have significant experience with microwave electronic circuit design, and measurement of antennas and circuits using vector network analysers and anechoic chambers. It would also be beneficial for the student to have a good understanding of semiconductor physics.

The main PhD supervisor will be Dr. Khalid Rajab (Antennas & Electromagnetics Group). Over the course of the PhD the student will have ample opportunity to collaborate with other members of the Group, as well as a number of partners at institutes throughout the UK and internationally. Informal enquiries about the studentship can be made by email to Dr. Rajab (k.rajab@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page

.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 28/08/15.

Interviews are expected to take place during the week of 14/09/15.

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PhD Studentship in Semantic Audio and Intelligent Music Production


Application closing date: 28/08/2015 Closed
Start date: 01/10/2015
Research group: Centre for Digital Music (C4DM)
Duration: 3 Years Funding available

Applications are invited for a fully-funded PhD studentship in the areas of Semantic Audio and Intelligent Music Production to seek novel ways of interacting with audio in music production, particularly in the recording studio. Topics of interest include but not limited to intelligent audio editing, feature extraction at source exploiting multitrack recordings, Semantic Web ontologies and ontology-based information management in music production. The research will involve working at the intersection of digital signal processing, machine learning, formal knowledge representation and human-computer interaction.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Sound and Music Computing or equivalent. Candidates should be confident in digital signal processing, machine learning, and/or knowledge representation and logic-based reasoning. Programming experience e.g. in Python, MATLAB, Prolog, Lisp, R, C/C++, Java or similar is essential. Prior experience in sound engineering and web technologies are welcome. An existing track record of publications and experience in music software development are very advantageous.

All nationalities are eligible to apply for this studentship which will start in the Autumn of 2015. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,057 per annum. The PhD supervisors will be Dr Gyorgy Fazekas and Prof Mark Sandler. The project will be based in the School of EECS, and the student will become a member of the interdisciplinary Centre for Digital Music (C4DM).

Informal enquiries can be made by email to Dr Fazekas (g.fazekas@qmul.ac.uk).

To apply, please follow the on-line process at (http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html) click on list of Research Degree Subjects and select ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page. The application requires a brief statement of research interests (approx. 500 words) which should answer why you are interested in the topics above and describe any relevant experience you have. A sample of written work (excerpt of final year dissertation or published academic paper) is also required. There is scope to tailor the research to the interests and skills of the successful candidate.

More details can be found at (http://www.eecs.qmul.ac.uk/phd/how-to-apply)

The closing date for the applications is 28 August 2015.

Interviews are expected to take place during the 2nd week of September.

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Two PhD Studentship in Semantic Audio and Music Informatics


Application closing date: 31/07/2015 Closed
Start date: 01/10/2015
Research group: Centre for Digital Music (C4DM)
Duration: 3 years Years Funding available

Applications are invited for 2 fully-funded PhD studentships, allied to the 5 year EPSRC and Digital Economy funded Programme Grant: Fusing Audio and Semantic Technologies for Intelligent Music Production and Consumption (FAST-IMPACt or FAST). The grant started in June 2014 and runs until June 2019. It is hosted by the Centre for Digital Music.

FAST-IMPACt aims to answer questions such as: How can next generation web technologies (Ontologies, Linked Data, Metadata) combined with music content analysis in the studio bring new value and functionality to producers, creators, consumers and intermediaries of music content? And how will both ends of the music value chain benefit from more engaging interactions (enhanced productivity, increased enjoyment and immersion) while creating or consuming music? And can intermediaries add value with semantically enhanced services?

Helping us pursue this vision are national and international partners from academia and industry, including BBC R&D, Abbey Road, Omnifone, Universal Music, Solid State Logic, International Audio Labs and more.

The two PhD appointments will be within the following areas (or similar) all related to signal processing:

• Sinusoidal models for note and phrase level representations (e.g. for modelling of ornamentation and intonation, absolute and relative tuning of multiple instruments, vibrato and tremolo effects) used in studios

• Music Transcription in the studio using mixed Harmonic Models, NMF and Informed Source Separation

• Compression for multi-track song representations, including complex, content-derived metadata

• Music features from Stereo & Surround Sound recordings: research & development for advanced applications

• New perceptually driven features incorporating advanced models of human hearing

• Markov logic networks for knowledge representation and processing from music signals

• Context-driven audio rendering from stems for optimal listener experience in changing acoustic environments

• In-song browsing and navigation using stems to derive music structure, for enhanced listener experience

• Music collection navigation and browsing for enhanced listener experience

All nationalities are eligible to apply for this studentship, which will start in Autumn 2015. The studentships are for 4 years, and cover student fees as well as a tax-free stipend of £15,863 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Sound & Music Computing or equivalent. Candidates should be confident in digital signal processing and/or machine learning, and have programming experience in, e.g. MATLAB, Mathematica, Python, Java, C++ or similar. Prior experience with Semantic Web is welcome. Experience in research and a track record of publications is very advantageous. Formal music training or sound engineering experience is also advantageous.

Informal enquiries can be made by email to Mark Sandler (mark.sandler@qmul.ac.uk) who will lead the PhD supervision.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in one or more of the topics above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31/07/15. Interviews are expected to take place between 2 and 4 September 2015.

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PhD Studentships in audio-visual intelligent sensing


Application closing date: 08/06/2015 Closed
Start date: 01/10/2015
Research group: Centre for Intelligent Sensing
Duration: 3 Years Funding available

Applications are invited for 4 (four) PhD Studentships to undertake research in the areas of computer vision and audio processing for people monitoring in multi-camera multi-microphone environments, and will be part of an interdisciplinary project on audio-visual monitoring for smart interactive and reactive environments. The Studentships (to be started in or after September 2015) are part of an interdisciplinary project between the Centre for Intelligent Sensing (http://cis.eecs.qmul.ac.uk) at Queen Mary University of London (QMUL) and the Centre for Information Technology (http://ict.fbk.eu) at the Fondazione Bruno Kessler (FBK), Trento, Italy. The Project will focus on methods for long-term people tracking, activity recognition, acoustic scene analysis, behaviour analysis, distant-speech recognition and understanding applied to individuals as well as groups. Such information will enable learning 'patterns of usage' of the environment, and patterns can in turn be used to adapt and optimise the sensing accordingly. Each PhD student will spend approximatively 50% of their time in London and 50% of their PhD time in Trento and will have access to state-of-the-art audio-visual laboratories, including a multi-camera multi-microphone installation at a large open hallway and a smart home facility equipped with cameras, microphones and automated devices.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and should have good programming experience, in particular of C/C++ language and of MATLAB environment. Previous knowledge of Digital Signal Processing is a requirement. Previous knowledge of Computer Vision or Machine Learning or Audio Signal Processing and/or Speech Recognition is desired, but not required. Experience in using relevant libraries (e.g. OpenCV) is also desirable.

The studentships will be based at Centre for Intelligent Sensing in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Professor Andrea Cavallaro (http://www.eecs.qmul.ac.uk/~andrea/) and Dr Oswald Lanz (https://tev.fbk.eu/people/profile/lanz) or, depending on the type of the PhD project chosen by the candidate, Dr Maurizio Omologo (http://shine.fbk.eu/people/omologo). To apply please follow the on-line process at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting Electronic Engineering or Computer Science in the A-Z list of research opportunities and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest (no more than 500 words or one side of A4 paper) should state whether you are interested in a computer vision PhD project, or an audio processing PhD project, or an audio-visual processing PhD project. Moreover, your Statement of Research Interest should answer two questions: Why are you interested in the proposed area? What is your experience in the proposed area? In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php .

Informal enquiries can be made by email to Professor Andrea Cavallaro (a.cavallaro@qmul.ac.uk).

The closing date for the applications is 8 June 2015.

Interviews are expected to take place during the week commencing 22 June 2015.

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PhD Studentships in audio-visual intelligent sensing


Application closing date: 08/06/2015 Closed
Start date: 01/09/2015
Research group: Centre for Intelligent Sensing
Duration: - Years Funding available

Applications are invited for 4 (four) PhD Studentships to undertake research in the areas of computer vision and audio processing for people monitoring in multi-camera multi-microphone environments, and will be part of an interdisciplinary project on audio-visual monitoring for smart interactive and reactive environments. The Studentships (to be started in or after September 2015) are part of an interdisciplinary project between the Centre for Intelligent Sensing (http://cis.eecs.qmul.ac.uk) at Queen Mary University of London (QMUL) and the Centre for Information Technology (http://ict.fbk.eu) at the Fondazione Bruno Kessler (FBK), Trento, Italy.

The Project will focus on methods for long-term people tracking, activity recognition, acoustic scene analysis, behaviour analysis, distant-speech recognition and understanding applied to individuals as well as groups. Such information will enable learning 'patterns of usage' of the environment, and patterns can in turn be used to adapt and optimise the sensing accordingly.

Each PhD student will spend approximatively 50% of their time in London and 50% of their PhD time in Trento and will have access to state-of-the-art audio-visual laboratories, including a multi-camera multi-microphone installation at a large open hallway and a smart home facility equipped with cameras, microphones and automated devices.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and should have good programming experience, in particular of C/C++ language and of MATLAB environment. Previous knowledge of Digital Signal Processing is a requirement. Previous knowledge of Computer Vision or Machine Learning or Audio Signal Processing and/or Speech Recognition is desired, but not required. Experience in using relevant libraries (e.g. OpenCV) is also desirable.

The studentships will be based at Centre for Intelligent Sensing in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Professor Andrea Cavallaro (http://www.eecs.qmul.ac.uk/~andrea/) and Dr Oswald Lanz (https://tev.fbk.eu/people/profile/lanz) or, depending on the type of the PhD project chosen by the candidate, Dr Maurizio Omologo (http://shine.fbk.eu/people/omologo). To apply please follow the on-line process at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting Electronic Engineering or Computer Science in the A-Z list of research opportunities and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest (no more than 500 words or one side of A4 paper) should state whether you are interested in a computer vision PhD project, or an audio processing PhD project, or an audio-visual processing PhD project. Moreover, your Statement of Research Interest should answer two questions: Why are you interested in the proposed area? What is your experience in the proposed area? In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php .

Informal enquiries can be made by email to Professor Andrea Cavallaro (a.cavallaro@qmul.ac.uk).

Interviews are expected to take place during the week commencing 22 June 2015.

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PhD Studentship in Intelligent Machine Music Listening


Application closing date: 01/05/2015 Closed
Start date: 01/07/2015
Research group: Centre for Digital Music (C4DM)
Duration: 3 Years Funding available

Applications are invited for a fully-funded PhD studentship, to seek ways to exploit novel and holistic approaches to evaluation for building machine music listening systems (and constituent parts). A major emphasis will be on answering “how” systems work and “what” they have learned to do, in relation to the success criteria of real-world use cases. The research will involve working at the intersection of digital signal processing, machine learning, and the design and analysis of experiments.

All nationalities are eligible to apply for this studentship, which will start in Autumn 2015. The studentship is for three years, and covers student fees as well as a tax-free stipend of £15,863 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, or Mathematics. Candidates should be confident in digital signal processing or machine learning, and have programming experience in, e.g. R, MATLAB, or Python. Experience in research and a track record of publications is very advantageous. Formal music training is also advantageous.

The PhD supervisors will be Dr. Bob L. Sturm (Machine Listening) and Dr. Hugo Maruri-Aguilar (Statistics). Please see http://www.eecs.qmul.ac.uk/~sturm for background. The project will be based in the School of EECS, and the student will become a member of the interdisciplinary Centre for Digital Music. Informal enquiries can be made by email to Dr. Sturm (b.sturm@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 1/05/15.

Interviews are expected to take place /15.during the week of 15/06

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PhD Studentship in Large-Scale Dynamic Graph Sensing


Application closing date: 01/05/2015 Closed
Start date: 01/10/2015
Research group: Networks
Duration: 3 Years Funding available

Applications are invited for a fully-funded PhD studentship, to seek ways to efficiently process dynamic large-scale graph structures in near-real time. A major emphasis will be on understanding “what” analysis techniques are most relevant, in order to process real world use cases that rely on data coming from social networks, the Internet or content elements such as images. Based on that, the student will research to answer “how” to make big data systems work for these applications. The research will take place at the intersection of big data processing systems, and data mining algorithms for intelligent sensing. (http://cis.eecs.qmul.ac.uk/).

All nationalities are eligible to apply for this studentship, which will start in Autumn 2015. The studentship is for three years, and covers student fees as well as a tax-free stipend of £16,057 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, or Mathematics. Candidates should have strong programming experience, e.g. Scala, C, Java, or Python. Practical experience in large-scale processing platforms (e.g. Spark, Storm) would be desirable.

The main PhD supervisor will be Dr. Félix Cuadrado (Big Data Processing). The student will become a member of the interdisciplinary Centre for Intelligent Sensing. Over the PhD time the student will collaborate with academics from the Centre to apply large-scale graph processing techniques for sensing in multiple domains. Informal enquiries about the studentship can be made by email to Dr. Cuadrado (felix.cuadrado@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 1/05/15.

Interviews are expected to take place during the week of 15/05/15.

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PhD Studentship in Eye-Movement Analysis


Application closing date: 01/04/2015 Closed
Start date: 01/06/2015
Research group: Computer Vision
Duration: 3 Years Funding available

Applications are invited for a fully-funded PhD studentship, to conduct research into human eye-movements. The project will develop new computational models of visual behaviour, based on recent ideas from computer vision, spatial statistics and time-series analysis. The models will be tested in psychophysical experiments, and implemented in an open-source tool-kit.

All nationalities are eligible to apply for this studentship, which will start in Spring 2015. The studentship is for three years, and covers student fees as well as a tax-free stipend of £15,863 per annum.

Candidates must have a first-class honours degree or equivalent, or a good MSc Degree in Computer Science, Electronic Engineering, Physics, Mathematics, Computational Neuroscience, or Experimental Psychology. Experience of programming in e.g. R, Matlab, or Python is essential. Knowledge of mathematical or psychophysical methods would be an advantage.

The PhD supervisors will be Dr. Miles Hansard (Computer Vision) and Dr. Isabelle Mareschal (Experimental Psychology). The project will be part of ongoing research into geometric scene modelling, binocular vision, and visual attention. Please see http://www.eecs.qmul.ac.uk/~milesh/ and http://www.mareschallab.sbcs.qmul.ac.uk/ for background. The project will be based in the School of EECS, and informal enquiries can be made to Dr. Miles Hansard (miles.hansard@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 1/04/15.

Interviews are expected to take place during the week of 13/04/15.

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1 PhD studentship in “Software-defined networking”


Application closing date: 18/01/2015 Closed
Start date: 02/02/2015
Research group: Networks
Duration: 3 Years Funding available

Applications are invited for one PhD studentship starting in January 2014 within the Networks Research Group, under the supervision of Prof. Steve Uhlig. The topic areas for this studentship focuses on (but are not limited to) Internet-wide monitoring, software-defined networking (SDN), or distributed systems for SDN-based network management. For examples of the kind of work being undertaken by Prof. Steve Uhlig, see http://www.eecs.qmul.ac.uk/~steve/.

The studentship will be based in the School of Electronic Engineering and Computer Science (EECS) www.eecs.qmul.ac.uk at Queen Mary University of London, in the Networks Research Group.

The PhD student will be part of a growing and very active group that has over 15 members working in the broad area of networking and distributed systems. Open collaboration is strongly encouraged and expected between all members of the group. This studentship, funded within the EU project “Towards a flexible software-defined network ecosystem” (ENDEAVOUR) within the Horizon 2020 program, is for 3 years and will cover student fees and a tax-free stipend starting at £15,863 per annum (2014-15). Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science or electronic engineering. Please contact Prof. Steve Uhlig (steve.uhlig@qmul.ac.uk) if you would like to know more details.

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting “Electronic Engineering” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interests should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. Mark clearly the statement with Your Name and the title “Software-defined networking”. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 18th of January 2015.

Interviews will take place the week of the 19th of January 2015.

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PhD Studentship in Machine transcription of wildlife bird sound scenes


Application closing date: 12/01/2015 Closed
Start date: 01/07/2015
Research group: Centre for Digital Music (C4DM)
Duration: 3 Years Funding available

Applications are invited from all nationalities for a funded PhD Studentship starting July/August/September 2015 within the Centre for Digital Music (C4DM) at Queen Mary University of London, to perform cutting-edge research in "machine listening" - machine learning applied to sound. The PhD topic is "Machine transcription of wildlife bird sound scenes".

RESEARCH PROJECT: The goal of the project is to develop computational techniques for automatic transcription of sound recordings involving multiple birds. This has parallels with automatic transcription of music or of spoken conversation. The aim is to produce a system which, given a sound recording as input, outputs a transcript describing when each bird enters/leaves the scene, when it vocalises, and its species and other characteristics. The successful candidate will research and develop signal processing and machine learning methods suitable for noisy and multi-source audio recordings, and apply them to large databases of animal sound recordings. There is scope to tailor the project to the interests and skills of the successful candidate.

SKILLS: Candidates should have a first class honours degree or equivalent and/or a strong MSc degree in computer science, mathematics, physics, bioinformatics, computational biology or engineering. Good programming skills in either Python, Matlab, R or similar are essential, as is a passion for sound and/or nature. Knowledge of machine learning/data mining methods is desirable, but not essential if the candidate otherwise demonstrates good technical/mathematical skills.

Knowledge of birds and animal behaviour is also desirable but not essential.

SUPERVISION: The candidate will be supervised by Dr Dan Stowell (www.mcld.co.uk/research) and will join a group of around 60 full-time PhD students, post-doctoral researchers and academics in the C4DM (c4dm.eecs.qmul.ac.uk). The candidate will also interact with colleagues in the School of Biological and Chemical Sciences (SBCS) studying animal behaviour and vocal communication.

Informal enquiries can be made by email to Dr Dan Stowell: dan.stowell@qmul.ac.uk

This studentship is available to candidates of all nationalities. It is funded by the university for 3 years and will cover student fees and a tax-free stipend starting at £15,863 per annum.

To apply, please follow the online process (www.qmul.ac.uk/postgraduate/apply) by selecting 'Electronic Engineering' in the 'A-Z list of research opportunities' and following the instructions on the right-hand side of the web page. Make sure to state in your application that you are applying for the PhD with Dr Dan Stowell.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is Monday 12th January 2015.

Interviews are expected to take place during the week of 9th February 2015.

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PhD Studentship in machine learning applied to sound synthesis and media content creation


Application closing date: 16/12/2014 Closed
Start date: 02/02/2015
Research group: Centre for Digital Music (C4DM)
Duration: 3 Years Funding available

Applications are invited from all nationalities for a funded PhD Studentship starting January 2015 within the Centre for Digital Music (C4DM) at Queen Mary University of London, to perform cutting-edge research in machine learning applied to sound synthesis and media content creation.

RESEARCH PROJECT: In this PhD project, the concept of an Intelligent Assistant is investigated as a means of short form media content creation. A small high-tech company are in the process of creating a collaborative cloud platform for the creation of short form media, such as advertisements, promotional videos, local information etc. The Intelligent Assistant would identify and organise the content, add effects and synthesised sounds where necessary and present the produced content as a coherent story. It will be used as a tool by content creators to assist in quick and intuitive content creation. The goal of this project is to create and assess such tools, focusing on the challenges of varied, user-generated content with limited metadata, and the need for an enhanced user experience. Research questions to be investigated include;

- How best can sounds be synthesised in order to provide additional audio content to enhance the production? - Can multimedia (especially audio) content be intelligently combined to effectively tell a story? - How can this be assessed and evaluated? What are the key factors, features and metrics for intelligent storyboard systems?

This project is expected to generate high impact results, especially in the growing research fields of signal processing, sound synthesis, music informatics and semantic tools for content creation and production.

There is scope to tailor the project to the interests and skills of the successful candidate.

SKILLS: Candidates should have a first class honours degree or equivalent and/or a strong MSc degree in computer science, mathematics, physics or engineering, or equivalent experience. Good programming skills are essential, as is a passion for sound and/or nature. Knowledge of machine learning, digital signal processing or audio production is desirable, but not essential if the candidate otherwise demonstrates good technical/mathematical skills.

SUPERVISION: The candidate will be supervised by Dr Josh Reiss, and will join a group of around 60 full-time PhD students, post-doctoral researchers and academics in the Centre for Digital Music (c4dm.eecs.qmul.ac.uk). The candidate will also interact often with the company supporting the project, and with other researchers working on related projects.

Informal enquiries can be made by email to Dr. Josh Reiss: joshua.reiss@qmul.ac.uk

This studentship is available to candidates of all nationalities. It is funded by the university for 3 years and will cover student fees and a tax-free stipend starting at £15,863 per annum.

To apply, please follow the online process (www.qmul.ac.uk/postgraduate/apply) by selecting 'Electronic Engineering' in the 'A-Z list of research opportunities' and following the instructions on the right-hand side of the web page. Make sure to state in your application that you are applying for the PhD with Dr Josh Reiss.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is Tuesday 16th December 2015. Interviews are expected to take place during the week of 5th January 2015.

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EPSRC PhD CASE STUDENTSHIP


Application closing date: 19/09/2014 Closed
Start date: 03/11/2014
Research group: Antennas & Electromagnetics
Duration: 3 Years Funding available

Applications are invited for a PhD Studentship, to undertake research in the area of Metamaterials in collaboration with BAE Systems from February 2014, or as soon as possible thereafter. The studentship is based at the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Prof. Yang Hao (FIEEE) of the Antennas and Electromagnetics Group. The studentship will involve the development of an optimisation tool to design artificial magnetic materials for antenna and microwave applications. It is expected that the students will work in close collaboration with other researchers, both in QMUL and partner industry.

Candidates should have a first class honours degree or equivalent (and preferably a Masters Degree) in any relevant area including Electronic Engineering, Mathematics, Physics, or a related field, and the ability to demonstrate strong mathematical and analytical skills. Good programming skills and background in electromagnetics are also desirable.

This studentship, funded by an EPSRC Doctoral Training Account, is for fees plus a tax-free stipend starting at £15,726 per annum. It is a CASE award and attracts an additional stipend of £5,200 per annum from the industrial partner. Further details of the EPSRC scheme including terms and conditions can be found here: www.epsrc.ac.uk/skills/students/dta/Pages/dta.aspx Applicants must satisfy UK residence requirements as defined here: www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx

Informal enquiries can be made by email to Prof. Yang Hao at yang.hao@eecs.qmul.ac.uk.To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply/) by selecting “Electronic Engineering” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii)What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31st. March 2014. Interviews are expected to take place after 15th April 2014.

Valuing Diversity & Committed to Equality

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Experimenting with network measurements and evaluating user QoE


Application closing date: 18/07/2014 Closed
Start date: 01/10/2014
Research group: Networks
Duration: 3.5 Years Funding available

Applications are invited for a PhD Studentship starting in September 2014 to undertake research in network measurement on broadband packet based networks, using a testbed developed in the Networks research group of EECS QMUL (www.eecs.qmul.ac.uk/). The work will be supervised by Dr John Schormans (http://www.eecs.qmul.ac.uk/people/view/3142/dr-john-schormans) and Prof. Jonathan Pitts (http://www.eecs.qmul.ac.uk/people/view/3154/prof-jonathan-pitts), both of whom are members of the Networks research group of QMUL EECS (www.eecs.qmul.ac.uk/).

Motivation: It is now well accepted that user quality of experience (QoE) measures are a far better representation of network performance as experienced by the user than are raw quality of service (QoS) measures (e.g. packet loss probability, mean end-to-end packet delay, delay jitter). However evaluating QoE first requires accurate measurement of QoS metrics. State-of-the-art: Prior research by Schormans, Pitts and Jones (now at Intergence), has shown that poorly designed measurements can easily lead to results that are very inaccurate, e.g. 2 orders of magnitude in error for measured packet loss probability. When translated through QoS to QoE mapping this could be the difference between “good” and “highly unsatisfactory” user quality.

Goal: For this PhD research we plan the following objectives:

• Further develop the existing testbed at QMUL. The testbed features commercial standard scheduling algorithms, and facilitates experimentation with real-time injection of packet probes.

• By comparison with known delays and loss values, evaluate how measurements obtained from probing on the router compare with the known values, and evaluate information loss.

• Map delay and loss values to user QoE values, for both known values taken direct from the router, and those obtained by measurement. Evaluate the effects of measurement errors on discovered user QoE, and evaluate the margin of improvement achievable by adaptive active queue management (AQM) algorithms.

The candidate will work in collaboration with Intergence (http://www.intergence.com/), and will also be expected to undertake an internship at Intergence (they are based in Cambridge, UK). This studentship, funded by an EPSRC CASE award, is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,590 per annum, plus an additional stipend of £5,200 per annum. See (http://www.epsrc.ac.uk/funding/students/dta/Pages/default.aspx) for further details of the EPSRC scheme including terms and conditions. Applicants must be UK nationals or residents as defined here: http://www.epsrc.ac.uk/funding/students/pages/eligibility.aspx

Candidates should have a 1st or 2:1 honors degree or equivalent, or a strong masters degree, in computer science, electronic engineering or applied mathematics. Knowledge of LABVIEW, packet routers, and MATLAB would be an advantage.

Informal enquiries may be addressed to: John Schormans (j.schormans@qmul.ac.uk). A completed application form, a CV listing all publications, your representative publications in PDF format, 3 reference letters, a research statement and other relevant documents as requested (see www.eecs.qmul.ac.uk/phd/how-to-apply) must be submitted online following the instructions given in the link.

The closing date for the applications is 18/7/2014. Interviews are expected to take place during week commencing 21/7/2014.

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PhD Studentship in Machine Intelligence


Application closing date: 16/07/2014 Closed
Start date: 01/10/2014
Research group: Risk and Information Management (RIM)
Duration: 3 Years Funding available

Applications are invited for a funded PhD studentship to undertake research within the area of machine intelligence. All nationalities are eligible to apply for this studentship, which will start in September/October 2014.

The student will be based in the Risk and Information Management group at the Queen Mary University School of Electronic Engineering and Computer Science and will have the opportunity to collaborate with the Vision group. The student will be supervised by Dr Timothy Hospedales.

Depending on interests and experience, the student will have the opportunity to perform cutting-edge research in a variety of active research areas within these leading groups. These include topics related to life-long machine learning (transfer, multi-task and cross domain learning), deep learning and active learning. Current areas of application include computer vision and multi-media (recognition, person re-identification, attribute learning, weakly supervised learning), sentiment analysis, medical data analysis and internet/web data analytics.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics or Mathematics. Candidates must be confident in applied mathematics, and have programming experience. Previous experience in machine learning will be a key advantage.

Informal enquiries can be made by email to Dr Timothy Hospedales (t.hospedales@qmul.ac.uk).

This studentship is available to candidates of all nationalities. It is funded by the university for 3 years and will cover student fees and a tax-free stipend starting at £15,863 per annum.

To apply, please follow the online process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 16 July 2014.

Interviews are expected to take place during the week of 21 July 2014.

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Media & Arts Technology Programme 2014


Application closing date: 07/07/2014 Closed
Start date: 22/09/2014
Research group: Cognitive Science
Duration: 4 Years Funding available

PhD Positions: Media & Arts Technology Programme.

Fully Funded Ph.D. Scholarships Available for September 2014 ~ to apply go to: http://www.mat.qmul.ac.uk

For 2014 entry we welcome applicants with interests in: - media content, semantics and narrative for music, audio, video, social computing, interactive art, wearables and aspects of computational linguistics, psychology and cognitive science, applied to these fields.

Queen Mary University of London (QMUL) is one of the UK's leading research universities and is located at the heart of Europe's largest concentration of creative industries. We run an innovative four-year inter-disciplinary training programme in the science and technologies that are transforming the creative sector. Our aim is to produce PhD graduates who combine world-class technical and creative skills and who have a unique vision of how digital technology transforms creative possibilities, cultural experiences and social economies.

A final round of PhD places for entry in September 2014 have now been released. We are offering funded PhD scholarships (circa £15,726 p.a. including all fees) for direct entry to the PhD programme. To be eligible for these awards you must be a UK or EU national who has lived in the UK for the last three years.

Final deadline for applications is 7th July 2014.

You must be able to demonstrate academic achievement at the level of a first class honours degree and excellent critical and analytic skills. You should also be able to demonstrate a clear aptitude for interdisciplinary research and programming or mathematical ability. We welcome applicants with strong backgrounds in architecture, cognitive science, computer science, electronic engineering, interactive multimedia technologies, industrial design, psychology, or cognate disciplines.

You will also develop a working partnership with one of our strategic collaborators including: BBC, BT, Furtherfield, Gracenote, The Barbican, V+A, Orange R+D, Proctor and Gamble, Goldsmiths Department of Computing, Illustrious, Inition, last.fm, leanmeanfightingmachine, Meridian, Seeper, SONY Computer Entertainment Europe, [space], Cinimod, Troika, The Union Chapel, United Visual Artists, V2_, Wildpalm, Smartlab, Culturelab, FXpansion, Seed Media New York.

To apply go to: http://www.mat.qmul.ac.uk Email: mat-enquiries@eecs.qmul.ac.uk for further details.

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AHRC Doctoral Studentship in Computational Musicology


Application closing date: 01/07/2014 Closed
Start date: 01/10/2014
Research group: Cognitive Science
Duration: 3.5 Years Funding available

We invite applications for a Doctoral Studentship, funded by the Arts and Humanities Research Council, in Computational Musicology, located at Queen Mary University of London, under the supervision of Professor Geraint Wiggins.

The studentship is part of the "Transforming Musicology" project, including Goldsmiths, University of London, Queen Mary University of London, the University of Oxford and Lancaster University. This project, led by Prof Tim Crawford in the Computing Department of Goldsmiths, University of London, brings together 15 researchers to effect a Digital Transformation of the discipline of musicology.

The aim of the open studentship is to research and develop new methods for the representation of, and inference about, music-theoretic and perceptual aspects of music, based on, but not restricted to, past work by Prof. Wiggins and colleagues. This will be deployed using Semantic Web technology.

The studentship will be located in a very rich research environment, first within the Transforming Musicology project, but also within the Computational Creativity Lab at QMUL, and the successful candidate will be encouraged to interact with other researchers in both of these contexts.

This studentship, funded by an AHRC Doctoral Training Account, is for fees plus a tax-free stipend starting at £15,726 per annum. Further details of the AHRC scheme including terms and conditions can be found here:

http://www.ahrc.ac.uk/Funding-Opportunities/Postgraduate-funding/Pages/Current-award-holders.aspx

Applicants must satisfy UK residence requirements as defined here:

http://www.ahrc.ac.uk/Funding-Opportunities/Documents/Guide%20to%20Student%20Eligibility.pdf

Candidates must have a first class or 2.i undergraduate degree or equivalent, either with a significant component of music theory, in which case evidence of exceptionally well-developed practical expertise in computing, including programming, will be required, or in computer science or equivalent, in which case evidence of formal training in music theory (e.g. to grade V or equivalent) will be required. Candidates with relevant postgraduate qualifications will be particularly welcome, especially if they are qualified in both music and computer science. Other relevant qualifications and/or areas of expertise include (but are not limited to): artificial intelligence, informatics, formal logic and automated reasoning, musicology, knowledge representation, deductive database theory. The successful applicant may be required to undertake relevant undergraduate and postgraduate interdisciplinary courses as part of the programme of study.

Informal enquiries can be made by email to Prof. Geraint Wiggins (geraint.wiggins@qmul.ac.uk). Please note that Prof. Wiggins is unable to advise, prior to interview, whether an applicant is likely to be selected. To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply) by selecting “Electronic Engineering” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work, such as your final year dissertation. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 1st July 2014.

Interviews are expected to take place during July 2014.

Valuing Diversity & Committed to Equality

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PhD Studentship in Rhythm perception and analysis


Application closing date: 10/05/2014 Closed
Start date: 01/10/2014
Research group: Centre for Digital Music (C4DM)
Duration: 3.5 Years Funding available

Applications are invited for a funded PhD studentship undertaking research into audio signal analysis. The place will start in September/October 2014.

The specific area of research will be related to the perception of rhythm in music. Although music theorists have put forward many hypotheses explaining rhythmic phenomena such as syncopation, swing and groove, many of these theories are yet to be rigorously tested. Through the use of psychophysics we wish to identify solid links between theory and percept.

The project will be based in the world renowned Centre for Digital Music at the Queen Mary University School of Electronic Engineering and Computer Science (http://www.eecs.qmul.ac.uk/c4dm). The student will be supervised by Dr Christopher Harte.

This studentship is funded by the EPSRC and as such is open to UK residents only. It is funded for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum.

Candidates should have a first-class honours degree, distinction Masters Degree or equivalent in Electronic Engineering or Computer Science. Candidates must be confident in applied mathematics, signal processing and music theory and should have strong programming skills. Previous experience of research in audio or music analysis is highly desirable. A track record including publications in the area would be looked upon very favourably.

Informal enquiries can be made by email to Dr Christopher Harte (christopher.harte@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications May 9th 2014. Interviews are expected to take place during late week of 12/5/ 2014.

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PhD studentship: “Machine learning for bioinformatics: integrating multi ‘omics and clinical data”


Application closing date: 16/04/2014 Closed
Start date: 01/10/2014
Research group: Risk and Information Management (RIM)
Duration: 3.5 Years Funding available

Background: Biologists are among the largest producers of data after astrophysicists and particle physicists. Recently molecular data emerging from high throughput experimental methods has been complemented by clinical data on a range of diseases. The application of machine learning to predict disease progression and clinical outcome using these multi-'omics and clinical datasets has the potential to have a significant impact on strategies for personalised medicine.

Research project: We plan to investigate the integration and analysis of multiple heterogeneous data types for the construction of network-based models of diseases. Initial approaches will involve the use of Bayesian methods for data integration. The ability to identify sub-networks that may be associated with various clinical features will be explored. The project involves the integration and modelling of large and complex datasets, the development, validation and application of novel machine learning techniques and the analysis of biological networks.

Supervision:

The candidate will be based in the School of Electronic Engineering and Computer Science, Queen Mary, Univ. of London and will be supervised by Dr Fabrizio Smeraldi (http://www.eecs.qmul.ac.uk/~fabri). Research will be carried out in close cooperation with Dr Mansoor Saqi at the European Institute for Systems Biology and Medicine (EISBM), Lyon, France.

Skills: Candidates should have a first class honours degree or equivalent and/or a strong MSc degree in computer science, mathematics, physics, bioinformatics or engineering. Good programming skills in either Python, Matlab, C/C++ or similar are essential, as is a strong mathematical background. Knowledge of machine learning and the basics of biology is desirable, but not essential; however, a strong interest in these topics is a key requirement, as they will constitute the core of the work.

Funding: EPSRC funding is available for 3.5 years and will cover student fees and a tax-free stipend starting at £15,863 per annum. Further details here: (www.epsrc.ac.uk/skills/students/dta/Pages/dta.aspx), including terms and conditions. Applicants must be UK nationals or residents as defined here: www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx

Please apply on-line at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting “Computer Science” in the “A-Z list of research opportunities” and follow the instructions on the right hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’, which should answer two questions: (i) Why are you interested in this project? (ii) What is your experience in the area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is April 11th 2014.

Interviews are expected to take place in the week starting April 14th 2014.

Please contact Dr Fabrizio Smeraldi (f.smeraldi@qmul.ac.uk) with any queries.

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PhD Studentships in Electronic Engineering and Computer Science


Application closing date: 11/04/2014 Closed
Start date: 01/10/2014
Duration: 3.5 Years Funding available

The School of Electronic Engineering and Computer Science at Queen Mary University of London has a number of EPSRC-funded (UK residence) studentships for exceptional PhD students to join our school. Applications will be considered in any area of Electronic Engineering and/or Computer Science matching our research interests, with preference given to certain priority topics. Applicants must satisfy UK residence requirements (see below).

This year the priority topics are:

a) Cognitive Robotics (with Dr Chrisantha Fernando - see http://www.jobs.ac.uk/job/AII997/phd-in-cognitive-robotics/

b) Bioinformatics (with Dr Fabrizio Smeraldi - see http://www.jobs.ac.uk/job/AIK191/phd-studentship-in-program-analysis/

For details of other research in EECS, see our research groups and topics at http://www.eecs.qmul.ac.uk/research/

Candidates should have a first class honours degree or equivalent, and/or a distinction-level Masters Degree, in Computer Science, Electronic Engineering, or a related field, preferably with some experience in independent research. Candidates must also satisfy the EPSRC UK residence requirements, as outlined at: www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx

The PhD is funded from EPSRC is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,726 per annum. Further details of the EPSRC scheme see (www.epsrc.ac.uk/skills/students/dta/Pages/dta.aspx) including terms and conditions.

Please examine our research interests at www.eecs.qmul.ac.uk/research/ and contact relevant potential supervisors to discuss possible topics of interest. For general enquiries contact Melissa Yeo m.yeo@qmul.ac.uk (administrative enquiries) or Simon Dixon s.e.dixon@qmul.ac.uk (academic enquiries).

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply) by selecting “Computer Science” or “Electronic Engineering" as appropriate in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page. In your 'Research Proposal' you should describe the topic or topics that you are interested in researching (preferably after discussing these with potential supervisors), and answer the two questions: (i) Why are you interested in the proposed area? (ii)What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. If you have a particular supervisor in mind, make sure that you indicate this person on the application form. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 11th. April 2014

Interviews are expected to take place in week of 14th. April 2014

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PhD in Cognitive Robotics


Application closing date: 11/04/2014 Closed
Start date: 01/10/2014
Research group: Cognitive Science
Duration: 3 Years Funding available

We are looking for an exceptionally motivated and creative PhD student to develop software based on the computational principles of the human brain. We wish to get closer to a future where computers can have values, are creative, and can understand the world. Our long-term goal is to exceed human intelligence. We’re looking for exceptional people to join us. If you want to work on the most important problems in the field with an international team who will challenge and support you, then this PhD is for you. We prefer you have a Masters in a relevant area, although we consider gifted applicants with other backgrounds. Good programming skills, and a solid foundation in maths is desirable. Knowledge of cognitive science and an interest in neuroscience is a plus. A willingness to learn about Deep Belief Networks, Reinforcement Learning and Machine Learning is important.

The PhD is funded from EPSRC is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,726 per annum. Further details of the EPSRC scheme see (www.epsrc.ac.uk/skills/students/dta/Pages/dta.aspx) including terms and conditions. Applicants must be UK nationals or residents as defined here: www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx

Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in Computer Science or a related field, preferably with some experience in independent creative thinking, and working as a group.

Please send enquires to Dr. Chrisantha Fernando at c.t.fernando@qmul.ac.uk or Skype chrisantha-f for an informal chat.

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply) by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page. Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii)What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 11th. April 2014

Interviews are expected to take place in week of 14th. April 2014

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PhD Studentship in Program Analysis


Application closing date: 11/04/2014 Closed
Start date: 01/10/2014
Research group: Theoretical Computer Science (Theory)
Duration: 3 Years Funding available

Applications are invited for a funded PhD studentship undertaking research into program analysis. All nationalities are eligible to apply for this studentship, which will start in September/October 2014.

The student will be a member of world renowned Theory Group at the Queen Mary University School of Electronic Engineering and Computer Science. The specific area of research will be program analysis with applications to optimization and verification of software. The student will be supervised by Dr Greta Yorsh.

Successful applicants should have a strong commitment to research and enjoy working at the intersection of theory and practice.

Required qualification and skills:

• Bachelor’s degree in Computer Science or related field

• Strong programming skills

• Basic knowledge in one or more of the following: advanced programming, algorithms and data structures, applied mathematics

Desirable qualification and skills:

• Master’s degree in Computer Science or related field

• Previous experience of research, in particular in areas related to programming languages, verification, and compilers

• A track record of publications

• Industrial experience in software development

• Proven experience of open source project development

• Contributions to open source projects such as GCC, LLVM, Linux

• Knowledge in one or more of the following: advanced programming, algorithms and data-structures, programming languages design and implementation, verification, program analysis and optimization, computer architecture, operating systems

Informal enquiries can be made by email to Dr Greta Yorsh (g.yorsh@qmul.ac.uk).

This studentship is available to candidates of all nationalities. It is funded by the university for is for 3 years and will cover student fees and a tax-free stipend starting at £15,863 per annum.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 11 April 2014.

Interviews are expected to take place during the week of 14 April 2014.

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colour vision technology for reflective electrochromic display


Application closing date: 31/03/2014 Closed
Start date: 01/04/2014
Research group: Multimedia & Vision (MMV)
Duration: 3 Years Funding available

Applications are invited for one PhD studentship starting in April 2014. This studentship involves contribution to EU FP7 funded project ECOPIX (Eco-friendly digital advertising display, based on novel printable electrochromic polymers) and the practical outcomes of the research work are expected to influence emerging digital display field.

The studentship will be based in the School of Electronic Engineering and Computer Science (EECS) www.eecs.qmul.ac.uk at Queen Mary University of London, in the Multimedia and Vision Research Group. The project undertaken under this studentship is expected to fit into the wider research programme of the group, which has widespread recognition for its research in image processing and computer vision areas.

This studentship, funded by EU FP7 ECOPIX, is for 3 years and will cover student fees and a tax-free stipend starting in the range of £15,600 per annum plus a travel fund for attending collaborative meetings and international conferences. Candidates should have a First Class Honours or a good 2.1 degree in Mathematics, Electronic Engineering, Physics or Computer Science and have excellent computer programming skills. Experience with software design for embedded systems is required, with essential background in image or video processing techniques. Please contact Dr Qianni Zhang (qianni.zhang@qmul.ac.uk) if you would like to know more details, or have any queries about how to apply.

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting “Electronic Engineering ” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. Mark clearly the statement with Your Name and the title “colour vision technology for reflective electrochromic display”. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31 March 2014.

Interviews are expected to take place in February-March 2014.

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semantic image understanding and 3D reconstruction


Application closing date: 31/03/2014 Closed
Start date: 01/04/2014
Research group: Multimedia & Vision (MMV)
Duration: 3 Years Funding available

Applications are invited for one PhD studentship starting in April 2014. The focus of this doctoral research project is on object segmentation and classification for 3D reconstruction using descriptors from multiple modalities. The developed methodology will be employed and tested in medical applications.

The studentship will be based in the School of Electronic Engineering and Computer Science (EECS) www.eecs.qmul.ac.uk at Queen Mary University of London, in the Multimedia and Vision Research Group. The project undertaken under this studentship is expected to fit into the wider research programme of the group, which has widespread recognition for its research in image processing and computer vision areas.

This studentship, funded by the School of Electronic Engineering and Computer Science, is for 3 years and will cover student fees and a tax-free stipend starting at £15726 per annum. Applicants must be UK or EU nationals or residents as defined here: http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science, mathematics, or electronic engineering. Please contact Dr Qianni Zhang (qianni.zhang@qmul.ac.uk) if you would like to know more details, or have any queries about how to apply.

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting “Electronic Engineering ” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. Mark clearly the statement with Your Name and the title “semantic image understanding and 3D reconstruction”. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31 March 2014.

Interviews are expected to take place in February-March 2014.

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Resource Constrained Intelligent Sensing


Application closing date: 26/02/2014 Closed
Start date: 01/10/2014
Research group: Risk and Information Management (RIM)
Duration: 3 Years Funding available

PhD Studentship in Resource Constrained Intelligent Sensing

Applications are invited, for a funded PhD studentship, to undertake research into resource constrained intelligent sensing. All nationalities are eligible to apply for this studentship, which will start in September/October 2014.

Conventional approaches to machine perception apply supervised machine learning techniques to predict semantic quantities of interest from a fixed set of low-level features. This project aims to address the meta-learning challenges of deciding which features to extract, and which learning algorithm to apply on a dynamic case-by-case basis to do the best possible job within a constrained amount of computing resource.

The project will be part of the new interdisciplinary Centre for Intelligent Sensing at QMUL (http://cis.eecs.qmul.ac.uk/). The student will be supervised by Dr. Timothy Hospedales and Prof. Mark Plumbley (EECS). Two sources of funding are available:

• An EPSRC studentship is available to candidates with UK residency. This studentship is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum. Full details and eligibility conditions can be found at http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should state if they are eligible for this studentship.

• An International studentship is available to candidates without UK residency and is for 3 years. This studentship covers student fees and a tax-free stipend of £15,720 per annum.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and have programming experience. Previous experience in machine learning will be an advantage. Previous knowledge of computer vision or audio is not required.

I

nformal enquiries can be made by email to Dr Timothy Hospedales (t.hospedales@qmul.ac.uk). Please see also: http://www.eecs.qmul.ac.uk/~tmh/ and http://www.eecs.qmul.ac.uk/~markp/.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have, including mathematical modelling and programming? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for applications is Thursday 20 February 2014. Interviews are expected to take place on Wednesday 26th February 2014.

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Computer Vision based Analysis of Human Behaviour - Life Sciences Institute


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Multimedia & Vision (MMV)
Duration: 3 Years Funding available

Applications are invited, for a funded PhD studentship, to undertake research into Automatic non Verbal Behaviour Analysis in Diagnosis and Treatment of Mental Illnesses. The studentship is for EU/UK residents and will start in September/October 2014.

The focus of the project is on Machine Learning and Computer Vision methodologies for the (semi)-automatic analysis of non-verbal behaviour of people with mental illnesses in group therapy sessions. Within this context, we will analyse facial behaviour (e.g. smiles, head nods), body behaviour (e.g. hand gestures) and identify affective states (e.g. level of arousal) and types of interactions between members of the therapy groups (e.g. therapist-patient, patient-patient). We will also explore whether and how the individual behaviours and group interactions are affected by the situational and social context, e.g. one-to-one interaction or interaction with several parties, size and composition of the group, interaction at different stages of the group session.

The project is expected to build on recent developments in the field of Social Signal Processing and Affective Computing, and in recent advances in the field of the Computer Vision and Machine Learning in analysis of human behaviour in uncontrolled conditions ('in the wild'). Particular emphasis will be given on modelling the dynamics of human behaviour and group interactions.

The student will be supervised by Dr. Ioannis Patras (http://www.eecs.qmul.ac.uk/~ioannisp/‎) and Professor Stefan Priebe (http://www.stefanpriebe.com). The studentship will be hosted by the school of Electronic Engineering and Computer Science and by the Unit for Social and Community Psychiatry in the school of Medicine and Dentistry in the Wolfson Institute.

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering.

For informal enquiries, please contact Dr. Ioannis Patras at i.patras@qmul.ac.uk. Please make sure to put the string [LifeSciencesPhD] in the subject of your email

.

Please see also: http://www.eecs.qmul.ac.uk/~ioannisp/

The Award: Life Sciences Studentships are open to suitably qualified candidates from the UK & EU only. The Studentship consists of the full cost of tuition fees and £15,786 a year for maintenance for 3 years.

How to apply: To apply, please visit: http://www.qmul.ac.uk/lifesciences/phd/index.html

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have, including mathematical modelling and programming? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31 January 2014. Interviews are expected to take place during February 2014.

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Geometric Modelling of 3D Point-Cloud Data


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Computer Vision
Duration: 3 Years Funding available

Applications are invited, for a funded PhD studentship, to undertake research into 3D point-cloud representations of objects and scenes. All nationalities are eligible to apply for this studentship, which will start in September/October 2014.

The project will be concerned with modelling data from outdoor laser-scanners and indoor depth-cameras, as well as from multiple-view reconstructions. The primary objective is to develop new geometric representations of 3D point-clouds, based on recent work in applied mathematics and statistics (e.g. Discrete Differential Geometry and Topological Data Analysis). The new representations will be designed to help answer scientific questions about the natural environment, ranging from the statistics of 3D scene-structure to the processes of landscape geophysics.

The project will be based in the new interdisciplinary Centre for Intelligent Sensing at QMUL (http://cis.eecs.qmul.ac.uk/). The student will be supervised by Dr Miles Hansard and Prof Andrea Cavallaro (EECS), and will collaborate with Prof James Brasington (Geography). The student will have access to a variety of 3D capture-systems, including a long-range outdoor laser-scanner, as well as a large amount of existing geographic data. There will be opportunities to participate in fieldwork, coordinated by the School of Geography

.

Two sources of funding are available

:
  • An EPSRC studentship is available to candidates with UK residency. This studentship is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum. Full details and eligibility conditions can be found at http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should state if they are eligible for this studentship.
  • An International studentship is available to candidates without UK residency and is for 3 years.This studentship covers student fees and a tax-free stipend of £15,720 per annum
  • .

Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and should have some programming experience. Previous knowledge of Computer Vision and Geomatics is not required

Informal enquiries can be made by email to Dr Miles Hansard (miles.hansard@qmul.ac.uk). Please see also: http://www.eecs.qmul.ac.uk/~milesh/ and http://www.eecs.qmul.ac.uk/~andrea/.

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have, including mathematical modelling and programming? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31 January 2014

.

Interviews are expected to take place during February 2014.

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Music Perception & Cognition


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Cognitive Science
Duration: 3 Years Funding available

Applications are invited from all nationalities for a funded PhD studentship to undertake interdisciplinary research in music perception and cognition, to start in September/October 2014.

The candidate will be supervised by Dr Marcus Pearce (http://webprojects.eecs.qmul.ac.uk/marcusp/) and the research undertaken in this studentship will fit into the wider research programme of the Music Cognition Lab (http://music-cognition.eecs.qmul.ac.uk) based in the School of Electronic Engineering and Computer Science (http://www.eecs.qmul.ac.uk) and affiliated with the Division of Biological and Experimental Psychology (http://www.sbcs.qmul.ac.uk/research/researchdivisions/psychology).

Existing and ongoing projects in the Lab include:

  • Probabilistic modelling of musical expectation
  • The effects of cultural exposure on the perception of musical structure
  • Computational modelling of beat and rhythm perception
  • Probabilistic modelling of harmonic structure
  • Psychophysiological investigation of emotion induction and recognition in music
  • Modelling the acquisition of perceptual stylistic expertise by professional musicians
  • Effects of stimulus complexity on memory and liking for music
  • Predictive processing of prosody in language.

The Lab has a brand new soundproof facility for EEG studies of music perception and cognition

This studentship includes funding to cover student fees and a tax-free stipend starting at £15,726 per annum and will be available for 3 years (international students) or 3.5 years (UK students only).

Candidates should have a first class honours degree or equivalent, or a strong MSc Degree, in a relevant discipline, such as Psychology, Neuroscience, Computer Science, Music, Mathematics, Physics or Electronic Engineering, or equivalent experience. At least some musical training is preferable but not necessarily required.

Informal enquires can be made by email to Dr Marcus Pearce (marcus.pearce@qmul.ac.uk).

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your statement should answer three questions: (i) What is your proposed area of research? (ii) Why are you interested in the proposed area? (iii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31 January 2014

.

Interviews are expected to take place during February 2014

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Integrated Decision Making in Trauma Medicine


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Risk and Information Management (RIM)
Duration: 3 Years Funding available

Applications are invited for a funded PhD Studentship within the Risk and Information Management (RIM) Research Group in the field of medical decision support, to work on decision-making and prediction systems.

A successful collaboration with the Trauma Science group, in the Blizard Institute of the School of Medicine and Dentistry, has developed new tools using Bayesian networks to support clinical reasoning: one example greatly exceeds the performance of the existing ‘score’, requiring only the data available when a patient first arrives in hospital. The network models represent causal influences and are built using multiple sources of knowledge and data, supported by evidence from literature and experts.

The overall research goal is to develop and deploy probabilistic models of physiology and injury with a wider scope that can support multiple decisions, sufficiently well evidenced to be widely accepted, with automatic detection of relevant additions to the medical literature that may require the model to be revised. Improved decision support requires greater use of the explanatory capability of the models and improved interfaces; more automated model construction is also needed to reduce the costs of developing models. Depending on the candidate’s previous experience, it is likely that the initial task will be to develop and validate a new model for a clinical decision problem. He or she will then select specific research objectives contributing to the overall research goals.

The candidate will be supervised by Dr William Marsh, in the School of Electronic Engineering and Computer Science and will join a group of 12 PhD students and 3 post-doctoral researchers in the RIM group working on related aspects of decision support. The candidate will also be expected to collaborate closely with a researcher supervised by Mr Nigel Tai FRCS, a Consultant Vascular Surgeon at the Royal London Hospital, Whitechapel, and a Senior Lecturer in the Queen Mary School of Medicine and Dentistry. This collaboration provides an optimal setting for access to clinical data and knowledge as well as a context to validate the results of the research.

Candidates should have a first class honours degree or equivalent, or a strong MSc degree, in computer science, mathematics, physics or engineering. A good knowledge of probability and a strong interest in decision support are essential. Previous experience with Bayesian networks and competence in programming are desirable but not essential.

The studentships will be based in the School of Electronic Engineering and Computer Science (EECS) www.eecs.qmul.ac.uk at Queen Mary University of London.

Two sources of funding are available:

  • An EPSRC studentship is available to candidates with UK residency. This studentship is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum. Full details and eligibility conditions can be found at http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should state if they are eligible for this studentship
  • .
  • An International studentship is available to candidates without UK residency and is for 3 years. This studentship covers student fees and a tax-free stipend of £15,720 per annum
  • .

Please apply on-line at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’, which should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area, including probabilistic model and programming? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31 January 201

4.

Interviews are expected to take place during February 2014. Please contact William Marsh on d.w.r.marsh@qmul.ac.uk for any queries.

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Analysis of high dimensional multi ‘omics data for translational bioinformatics


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Computer Vision
Duration: 3 Years Funding available

Background: Large amounts of molecular data are emerging from high throughput experimental methods; more recently this data has been complemented by clinical data. The application of bioinformatic methodologies to predict disease progression and clinical outcome using these clinical and multi-'omics datasets has the potential to have a significant impact on strategies for personalised medicine.

Research project: A number of datasets are now becoming available in the public domain that can be used to explore and evaluate existing approaches and to develop new computational methods. This proposal involves the integration and analysis of multiple heterogeneous data types for the construction of disease networks. Initial approaches will involve the use of Bayesian methods for integration. The ability to identify sub-networks that may be associated with various clinical features will be explored. The project will involve integration and visualisation of large complex datasets, the development and application of machine learning methods and the analysis of biological networks.

Supervision: The candidate will be based in the School of Electronic Engineering and Computer Science, Queen Mary, University of London and will be supervised by Dr Fabrizio Smeraldi (http://www.eecs.qmul.ac.uk/~fabri). Research will be carried out in close cooperation with Dr Mansoor Saqi at the European Institute for Systems Biology and Medicine (EISBM), Lyon, France.

Skills: Candidates should have a first class honours degree or equivalent and/or a strong MSc degree in computer science, mathematics, physics, bioinformatics or engineering. Good programming skills in either Python, Matlab, C/C++ or similar are essential, as is a strong mathematical background. Knowledge of machine learning and the basics of biology is desirable, but not essential. The candidate will be expected to learn about these topics independently during the course of the Ph.D.

Two sources of funding are available

:
  • An EPSRC studentship is available to candidates with UK residency. This studentship is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum. Full details and eligibility conditions can be found at http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should state if they are eligible for this studentship.
  • An International studentship is available to candidates without UK residency and is for 3 years. This studentship covers student fees and a tax-free stipend of £15,720 per annum. Please apply on-line at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’, which should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper.

In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: http://www.eecs.qmul.ac.uk/phd/apply.php

The closing date for application is 31 January 2014.

Interviews are expected to take place during February 2014.

Please contact Dr Fabrizio Smeraldi (f.smeraldi@qmul.ac.uk) with any queries.

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Signal Processing and Data Mining Tools for the Analysis of Musical Evolution


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Centre for Digital Music (C4DM)
Duration: 3 Years Funding available

Applications are invited from all nationalities for a funded PhD Studentship starting September 2014 within the Centre for Digital Music (C4DM) in the field of music informatics research (MIR).

Research Project. The goal of the PhD is to study the emergence of musical styles, and to study empirically what causes styles to change using an evolutionary framework. The successful candidate will research and develop robust audio feature extractors and music data mining methods, and then apply them to the study of evolution in music corpora (Jazz, World, Popular, Classical).

Supervision. The candidate will be supervised by Dr Matthias Mauch (http://www.eecs.qmul.ac.uk/people/view/2932/dr-matthias-mauch) and will join a group of around 60 full-time PhD students, post-doctoral researchers and academics in the C4DM (http://c4dm.eecs.qmul.ac.uk/). The candidate will receive further advice on the study of evolution from external advisor Prof. Armand Leroi at Imperial College.

Background. Music informatics research (MIR) encompasses research in computational methods related to music; it is an engineering discipline with an emphasis on digital signal processing and machine learning. Combining engineering methods from MIR with evolutionary biology and musicology allows us to empirically study how music changes as it is created and selected by composers and listeners. Our prize-winning paper “Evolution of Music by Public Choice” (MacCallum et al., PNAS, 2012, http://www.pnas.org/content/early/2012/06/12/1203182109) exemplifies this new cross-disciplinary approach.

Skills. Candidates should have a first class honours degree or equivalent and/or a strong MSc degree in computer science, mathematics, physics, bioinformatics, evolutionary biology or engineering. Good programming skills in either Matlab, R, Python or similar are essential, as is a passion for music. Knowledge of machine learning/data mining methods is desirable, but not essential if the candidate otherwise demonstrates good technical/mathematical skills.

Two sources of funding are available:

  • An EPSRC studentship is available to candidates with UK residency. This studentship is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum. Full details and eligibility conditions can be found at http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should state if they are eligible for this studentship.
  • An International studentship is available to candidates without UK residency and is for 3 years. This studentship covers student fees and a tax-free stipend of £15,720 per annum.

Please apply on-line at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’, which should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area, including probabilistic model and programming? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31 January 2014.

Interviews are expected to take place during February 2014.

Please contact Dr Matthias Mauch (matthias.mauch@eecs.qmul.ac.uk) with any queries.

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Bio-inspired Cognitive Architectures for Open-Ended Learning


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Cognitive Science
Duration: 3 Years Funding available

A fully funded PhD is available to work as a part of our group studying Darwinian Neurodynamics, a new theory of the brain that explores how Darwinian principles may be integrated with supervised and unsupervised learning. The candidate will develop cognitive architectures capable of accumulation of adaptation, transfer learning, and creativity in robot control. Robots will learn a series of tasks. The skills acquired in earlier tasks will be reusable in later tasks, thus allowing incremental learning. The critical research question is what kinds of cognitive architecture are most suitable for transfer learning in reinforcement learning problems? Knowledge of machine learning, evolutionary computation, and reinforcement learning is highly desirable, but an interest and enthusiasm for developing complete models of cognition is key. Experience in computer vision, computational neuroscience, and cognitive science is also desirable.

You'll be working alongside an EU group that includes Prof. Eors Szathmary (Munich), Prof. Dario Floreano (EPFL), Prof. Luc Steels (Barcelona) on the FP-7 Funded Project INSIGHT, and a John Templeton Foundation funded project "Bayes, Darwin and Hebb". This will additionally fund visits to partners in Europe where you will present your work. You will be expected to also work as part of robozoo.co.uk to test your cognitive architectures in a range of 3D printed robots, the Nao Humanoid robot, and other robotic systems for sale to the general public.

The PhD is funded from either EPSRC (3.5 years UK Residents only) or an International studentship (3 years).

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

I request a statement of research interest that should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition I would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

Also you are strongly advised to contact me to arrange an informal discussion and to obtain our research papers in Darwinian neurodynamics prior to your application.

The closing date for the applications is 31st. January 2014

Interviews are expected to take place in Mid February 2014

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Audio signal analysis


Application closing date: 01/02/2014 Closed
Start date: 01/10/2014
Research group: Centre for Digital Music (C4DM)
Duration: 3 Years Funding available

Applications are invited for a funded PhD studentship undertaking research into audio signal analysis. All nationalities are eligible to apply for this studentship, which will start in September/October 2014.

The specific area of research will be related to new auditory models using audio power modulation spectra to extract auditory objects from audio signals. A wide range of audio- and specifically music-related research pursuits may benefit from these new models so the project will likely touch on several areas including human hearing, music perception and music informatics.

The project will be based in the world renowned Centre for Digital Music at the Queen Mary University School of Electronic Engineering and Computer Science (http://www.eecs.qmul.ac.uk/c4dm). The student will be supervised by Dr Christopher Harte.

This studentship is available to candidates of all nationalities. It is funded by the university for is for 3 years and will cover student fees and a tax-free stipend starting at £15,720 per annum.

C

andidates should have a first-class honours degree, distinction Masters Degree or equivalent in Electronic Engineering or Computer Science. Candidates must be confident in applied mathematics, signal processing and music theory and should have strong programming skills. Previous experience of research in audio or music analysis is highly desirable. A track record including publications in the area would be looked upon very favourably.

I

nformal enquiries can be made by email to Dr Christopher Harte (christopher.harte@qmul.ac.uk).

To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Electronic Engineering’ in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement should answer two questions: (i) Why are you interested in the topic? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

T

he closing date for the applications is 31 January 2014. Interviews are expected to take place during February 2014.

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Large scale analysis of forensic data


Application closing date: 01/02/2014 Closed
Start date: 03/03/2014
Research group: Multimedia & Vision (MMV)
Duration: 3 Years Funding available

Applicants from any nationality are invited for a fully funded PhD studentship on large scale analysis of forensic data for event-based representation and discovery.

The aim of this studentship is to study is to pursue a PhD degree by conducting research on a topic related to knowledge representation, reasoning to quantify uncertainty through fuzzy analysis, extraction of data patterns and anomalies and knowledge visualization to assist forensic analysis. The studentship involves a close collaboratio

n with industrial players and other academic institutions across Europe, as well as, Scotland Yard in London.

Candidates should have a First Class Honours or a good 2.1 degree in Mathematics, Electronic Engineering, Physics or Computer Science and have excellent computer programming skills. Experience with image or video processing techniques is also desirable.

Informal enquiries should be addressed to Prof. Ebroul Izquierdo at ebroul.izquierdo@eecs.qmul.ac.uk.

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply/) by selecting “Electronic Engineering” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Details about the school can be found at www.eecs.qmul.ac.uk

Funding Notes:

The scholarship will cover all study fees and a living stipend for three years. The stipend is in the range £15,600 per annum plus a travel fund for attending collaborative meetings and international conferences.

The closing date for the applications is 31 January 2014.

Interviews are expected to take place during February 2014.

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Large-scale Internet analytics


Application closing date: 04/12/2013 Closed
Start date: 03/03/2014
Research group: Networks
Duration: 0 Years Funding available

Applications are invited for one PhD studentship starting in September 2013 within the Networks Research Group, under supervision of Prof. Steve Uhlig. The topic areas for this studentship includes Internet-wide measurements, network analytics, and Cloud networking. For examples of the kind of work being undertaken see http://www.eecs.qmul.ac.uk/~steve/.

The studentship will be based in the School of Electronic Engineering and Computer Science (EECS) www.eecs.qmul.ac.uk at Queen Mary University of London, in the Networks Research Group. This studentship, funded by the School of Electronic Engineering and Computer Science, is for 3 years and will cover student fees and a tax-free stipend starting at £15,726 per annum. Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science or electronic engineering. Please contact Prof. Steve Uhlig (steve.uhlig@qmul.ac.uk) if you would like to know more details, or have any queries about how to apply.

To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting “Electronic Engineering” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. Mark clearly the statement with Your Name and the title “Large-scale Internet analytics”. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

The closing date for the applications is 31st January 2014.

Interviews are expected to take place in early February 2014.

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Visualizing the structure of information streams for aiding diagnosis/troubleshooting


Research group: Networks
Funding available

 

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Fully funded PhD studentship on video compression with specific focus on the emerging HEVC standard


Research group: Multimedia & Vision (MMV)
Funding available

Open PhD position for UK nationals only at the Multimedia & Vision Research Group, Queen Mary University of London (QMUL) and The BBC Research & Development

Applications from UK nationals are invited for a fully funded PhD studentship on video compression with specific focus on the emerging HEVC standard. The aim of this studentship is to study and optimise motion compensation techniques for high efficiency video coding. The studentship involves a close collaboration between the BBC and QMUL. The practical outcomes of the research work are expected to influence practical implementations of emerging video coding standards.

Candidates should have a First Class Honours or a good 2.1 degree in Mathematics, Electronic Engineering, Physics, Computer Science or any other related discipline and have excellent computer programming skills. Experience with image or video coding techniques as well as some background in image or video processing is desirable but not essential.

Most of the work will be carried out at the Queen Mary University of London under supervision of Prof Ebroul Izquierdo. The research student will often visit BBC R&D South Lab located in Centre House in West London. The research student is expected to spend at least nine months working at the BBC premises.

The interested candidates are encouraged to send their Curriculum Vitae and a cover letter to Prof. Ebroul Izquierdo (ebroul.izquierdo@eecs.qmul.ac.uk) or Dr Marta Mrak (marta.mrak@bbc.co.uk).

Funding Notes: The grant will cover all study fees for UK nationals/residents and a living stipend for three years. The stipend is in the range £21,000 per annum plus a travel fund for attending collaborative meetings and international conferences. Non-UK nationals/residents are not eligible for this studentship.

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